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Examples of LinkedIn Endorsements: Practical Patterns for AI Agents, RevOps Teams, and Technical Profiles

LinkedIn endorsements work best when they validate specific, observable skills, not vague popularity. For developers and AI engineers, strong endorsement strategies focus on skills such as Python, LLM...

Examples of LinkedIn Endorsements: Practical Patterns for AI Agents, RevOps Teams, and Technical Profiles

Author: Fintalio

TL;DR

LinkedIn endorsements work best when they validate specific, observable skills, not vague popularity. For developers and AI engineers, strong endorsement strategies focus on skills such as Python, LLMOps, API integration, RevOps automation, Salesforce, data pipelines, and prompt evaluation. An AI agent can handle the repetitive 80%, contact organization, CSV parsing, sequence setup, and follow-up tracking, while humans handle the judgment-heavy 20%.

What are good examples of LinkedIn endorsements?

Good examples of LinkedIn endorsements are skill validations that match a person’s actual work, role, and credibility goal. For a developer, that might be endorsements for Python, API Design, Kubernetes, or Machine Learning. For an AI engineer, it might be LLMOps, Retrieval-Augmented Generation, Prompt Engineering, or Vector Databases. For a RevOps builder, it might be Salesforce, HubSpot, CRM Architecture, or Workflow Automation.

A strong LinkedIn endorsement strategy does not chase every possible skill. It makes the top skills on a profile reflect the work the person wants to be trusted for.

For example:

  • A backend engineer should prioritize endorsements for: API Design, Python, PostgreSQL, Distributed Systems, Cloud Architecture
  • An AI engineer should prioritize: Machine Learning, LLMOps, Prompt Engineering, RAG, Python
  • A RevOps engineer should prioritize: Salesforce, Revenue Operations, CRM Automation, Data Enrichment, GTM Systems
  • A technical founder should prioritize: Product Strategy, SaaS, AI Automation, Go-to-Market Strategy, Systems Architecture
  • A solutions architect should prioritize: Enterprise Architecture, Integrations, Cloud Computing, Technical Discovery, Stakeholder Management

The 80/20 view is simple: the boring 80% is identifying contacts, segmenting them, preparing the right ask, and tracking progress. The human 20% is deciding who should be asked, which skills are truthful, and when the request would feel appropriate.

That is where autonomous agents can help, especially when they operate around a first-party session, contact data, and approved sequence infrastructure rather than pretending that endorsements can be manufactured.

LinkedIn endorsements vs recommendations

Before listing examples, it is important to separate two LinkedIn features that are often confused.

LinkedIn endorsements are skill validations. A connection endorses another person for a skill listed on the profile, such as Python, Sales Operations, or Cloud Computing.

LinkedIn recommendations are written testimonials. They usually describe a working relationship, a project, and a result.

Endorsements are lighter weight. Recommendations are deeper. A technical profile usually benefits from both, but endorsements are easier to collect at scale because they ask for a smaller action.

A practical profile strategy might look like this:

Technical credibility stack

[Skills listed on profile]
          |
          v
[Endorsements from relevant peers]
          |
          v
[Recommendations from managers, customers, or collaborators]
          |
          v
[Case studies, posts, demos, repositories, talks]

For readers comparing endorsement examples and profile positioning, the related guide on linkedin endorsements examples can help frame the broader profile strategy.

Why endorsements matter for developers and AI engineers

Endorsements are not a hiring guarantee, a sales shortcut, or a substitute for demonstrable work. They are a trust signal.

For developers and AI engineers, that signal matters because many technical profiles are difficult for non-technical buyers, recruiters, and operators to evaluate quickly. A profile that says “LLMOps” with endorsements from real colleagues, customers, and engineering leaders is easier to understand than a profile with a scattered skill list.

A well-built endorsement layer can support:

  • Recruiter discovery and qualification
  • Founder credibility during investor or partner research
  • Consultant trust during sales cycles
  • Developer advocate credibility
  • AI engineer positioning in a crowded market
  • RevOps specialist proof around CRM, automation, and systems work

The honest limitation: endorsements do not prove depth. They prove that other people associate a person with a skill. The best strategy is to align endorsements with real evidence, such as shipped systems, technical content, demos, implementation work, or customer outcomes.

Examples of LinkedIn endorsements by technical role

The following examples focus on skills that are specific enough to mean something, but broad enough that connections can confidently endorse them.

1. Backend developer endorsement examples

A backend developer should avoid making the profile look like a random inventory of every library used once. The strongest examples point to durable capabilities.

Recommended endorsement targets:

  • Python
  • Java
  • Node.js
  • API Design
  • REST APIs
  • PostgreSQL
  • Redis
  • Microservices
  • Distributed Systems
  • System Design
  • Cloud Computing
  • AWS
  • Docker
  • Kubernetes
  • CI/CD

Example positioning:

Primary skill cluster:
API Design, Python, PostgreSQL, Distributed Systems, AWS

Secondary skill cluster:
Docker, Kubernetes, CI/CD, Redis, Microservices

Best people to ask:

  • Engineering managers
  • Senior engineers who reviewed code
  • Product managers who worked with the developer
  • DevOps colleagues
  • Technical co-founders
  • Customers who interacted with APIs or integrations

Human judgment matters here. An AI agent can group contacts and prepare tasks, but it should not decide that a casual conference contact is qualified to endorse “Distributed Systems.”

2. AI engineer endorsement examples

AI profiles often suffer from buzzword overload. Endorsements help only when they map to work the person has actually delivered.

Recommended endorsement targets:

  • Machine Learning
  • Artificial Intelligence
  • Python
  • Natural Language Processing
  • LLMOps
  • Prompt Engineering
  • Retrieval-Augmented Generation
  • Vector Databases
  • Model Evaluation
  • Data Engineering
  • MLOps
  • LangChain
  • OpenAI API
  • Embeddings
  • Agentic Workflows

Example positioning:

Primary skill cluster:
LLMOps, Retrieval-Augmented Generation, Python, Model Evaluation, Vector Databases

Secondary skill cluster:
Prompt Engineering, Embeddings, Data Engineering, Agentic Workflows

Good endorsement sources:

  • ML leads
  • Product owners for AI features
  • Data engineers
  • Founders who shipped AI products with the engineer
  • Customers who reviewed AI workflow outcomes
  • Security or compliance reviewers, when relevant

For autonomous agent builders, the best endorsements are often the ones that separate “played with tools” from “shipped production-grade systems.” A person endorsed for LLMOps, model evaluation, and RAG looks more operationally credible than someone endorsed only for generic AI.

3. RevOps engineer endorsement examples

RevOps profiles should make operational reliability visible. The most useful endorsements focus on systems, data, handoffs, automation, and revenue process design.

Recommended endorsement targets:

  • Revenue Operations
  • Salesforce
  • HubSpot
  • CRM Architecture
  • Workflow Automation
  • Sales Operations
  • Marketing Operations
  • Data Enrichment
  • Lead Routing
  • Pipeline Management
  • GTM Systems
  • Reporting Automation
  • Process Optimization
  • Customer Lifecycle Management
  • API Integrations

Example positioning:

Primary skill cluster:
Revenue Operations, CRM Architecture, Salesforce, Workflow Automation, GTM Systems

Secondary skill cluster:
Lead Routing, Reporting Automation, API Integrations, Pipeline Management

Relevant endorsers:

  • Sales leaders
  • Marketing operations managers
  • Customer success leaders
  • Founders
  • CRM admins
  • Data engineers
  • Implementation partners

This is where the RevOps-honest view matters. Endorsements are not revenue attribution. They are soft proof that the person can be trusted around systems that affect revenue. They should support the evidence, not replace it.

4. Developer relations endorsement examples

Developer relations professionals need endorsements that show a mix of technical fluency, education, and community trust.

Recommended endorsement targets:

  • Developer Relations
  • Technical Writing
  • Public Speaking
  • API Documentation
  • Community Management
  • Developer Experience
  • JavaScript
  • Python
  • Product Marketing
  • Technical Content
  • Open Source
  • Workshops
  • SDKs
  • API Design

Example positioning:

Primary skill cluster:
Developer Relations, Technical Writing, API Documentation, Developer Experience

Secondary skill cluster:
Public Speaking, Community Management, SDKs, Open Source

Best endorsement sources:

  • Developers in the community
  • Product managers
  • Engineering teams
  • Event organizers
  • Open-source maintainers
  • Startup founders

A DevRel profile should not look purely like marketing or purely like engineering. Endorsements can help prove the hybrid nature of the work.

5. Solutions architect endorsement examples

A solutions architect often needs to show trust across commercial, technical, and enterprise conversations.

Recommended endorsement targets:

  • Solutions Architecture
  • Enterprise Architecture
  • Cloud Computing
  • API Integrations
  • Technical Discovery
  • Stakeholder Management
  • SaaS
  • Security Architecture
  • System Design
  • Pre-Sales
  • Technical Consulting
  • Data Architecture
  • Integration Architecture

Example positioning:

Primary skill cluster:
Solutions Architecture, API Integrations, Cloud Computing, Technical Discovery

Secondary skill cluster:
Stakeholder Management, Enterprise Architecture, Pre-Sales, Security Architecture

Best endorsement sources:

  • Account executives
  • Customer engineers
  • Enterprise customers
  • Product teams
  • Implementation managers
  • Security reviewers

The most valuable endorsements for this role come from people who have seen the architect translate messy business requirements into a working technical path.

6. Technical founder endorsement examples

Technical founders need to balance builder credibility with business credibility.

Recommended endorsement targets:

  • SaaS
  • Product Strategy
  • Artificial Intelligence
  • Software Development
  • Go-to-Market Strategy
  • Fundraising
  • Systems Architecture
  • API Design
  • Leadership
  • Startup Development
  • Revenue Operations
  • Automation
  • Product Management

Example positioning:

Primary skill cluster:
Product Strategy, SaaS, Systems Architecture, Artificial Intelligence, Leadership

Secondary skill cluster:
Go-to-Market Strategy, API Design, Automation, Revenue Operations

Good endorsement sources:

  • Co-founders
  • Investors
  • Early customers
  • Senior engineers
  • Advisors
  • Design partners
  • Operators

A founder should be especially careful not to over-index on trendy skills. If the company sells AI automation, endorsements for AI, systems architecture, and product strategy are useful. If the founder is primarily commercial, endorsements should not pretend otherwise.

Examples of LinkedIn endorsement request messages

Endorsements are requested by humans, not fabricated by automation. A good request is short, specific, and easy to reject.

The request should usually include:

  • The relationship context
  • The specific skills
  • A reason the person is qualified to endorse
  • A no-pressure exit

Here are practical examples.

Example 1: Developer asking a former engineering manager

Hi [Name], it was great working together on [Project]. If the experience still feels accurate, would you be open to endorsing a few skills on LinkedIn, especially API Design, Python, and System Design? No pressure at all, only if those match your view of the work.

Example 2: AI engineer asking a product lead

Hi [Name], since the team shipped the AI workflow for [Use Case], would you be comfortable endorsing skills such as LLMOps, RAG, and Model Evaluation on LinkedIn? Only if those feel fair based on the project.

Example 3: RevOps engineer asking a sales leader

Hi [Name], after the routing and CRM cleanup work for [Team/Region], would you be open to endorsing Revenue Operations, Salesforce, and Workflow Automation on LinkedIn? No problem if not, wanted to ask only because those skills came up directly in the project.

Example 4: Consultant asking a client

Hi [Name], if the implementation work was useful, would you be comfortable endorsing a few relevant LinkedIn skills, such as API Integrations, CRM Architecture, and Process Optimization? Only if that reflects your experience.

Example 5: Founder asking an advisor

Hi [Name], your perspective on the product and GTM work has been valuable. If it feels accurate, would you be open to endorsing Product Strategy, SaaS, and Go-to-Market Strategy on LinkedIn?

The pattern is consistent: specific, truthful, low-friction.

How an AI agent can support LinkedIn endorsement workflows

An autonomous agent should not try to create endorsements. That action belongs to real LinkedIn members inside the platform experience. The agent’s job is to run the boring 80% around contact organization, segmentation, preparation, and tracking.

A practical workflow can use a hosted LinkedIn relay or first-party session to reason around account status and contact records, then combine that with sequence infrastructure.

Only verified MCP tools should be used for this workflow. The available toolset is:

  • ListContacts
  • GetContact
  • ListContactGroups
  • ListSequences
  • GetSequence
  • ListSequenceTemplates
  • GetSequenceTemplate
  • ListVariables
  • GetAccountStatus
  • CreateContactGroup
  • UpdateContact
  • PauseSequence
  • ResumeSequence
  • StopSequence
  • ParseCsv
  • CommitCsv
  • CreateSequenceTemplate
  • CreateContact
  • LaunchSequence

A simple architecture looks like this:

                 +-----------------------------+
                 | Human owner                 |
                 | approves skills and people  |
                 +--------------+--------------+
                                |
                                v
+----------------+      +-------+--------+      +--------------------+
| Contact source | ---> | AI agent       | ---> | Contact groups     |
| CSV or records |      | segmentation   |      | by relationship    |
+----------------+      +-------+--------+      +---------+----------+
                                |                         |
                                v                         v
                       +--------+---------+      +--------------------+
                       | Sequence template| ---> | LaunchSequence     |
                       | draft and review |      | approved workflow  |
                       +------------------+      +--------------------+

The human stays in control of:

  • Which skills are legitimate
  • Which contacts are appropriate
  • Whether the request timing is respectful
  • Whether a sensitive customer relationship should be excluded
  • Whether the message tone matches the relationship

The agent handles:

  • Listing contacts
  • Reading contact details
  • Grouping by relationship type
  • Parsing CSV imports
  • Creating contact records
  • Creating contact groups
  • Updating contact metadata
  • Preparing sequence templates
  • Launching approved sequences
  • Pausing or stopping sequences when needed

Readers evaluating platform automation around LinkedIn workflows can also review linkedin sales navigator for adjacent prospecting and account research considerations.

MCP workflow example: endorsement request preparation

The following example is conceptual, but it stays within the verified MCP tool boundary.

Step 1: Check account status

The agent starts with GetAccountStatus to confirm that the connected account state is healthy before any workflow is prepared.

Agent action:
GetAccountStatus

Human decision:
If the account is not healthy, stop. Do not launch any workflow.

Step 2: Import and parse a contact list

If the team has a CSV of prior colleagues, customers, or collaborators, the agent can use ParseCsv.

CSV columns:
email, first_name, last_name, company, relationship_type, project, suggested_skills

Then the agent can use CommitCsv after validation.

Agent action:
ParseCsv -> review fields -> CommitCsv

Step 3: Create or update contacts

For missing records, the agent can use CreateContact. For existing records, it can use UpdateContact.

Decision rule:
If contact exists, update relationship_type and suggested_skills.
If contact does not exist, create contact with approved fields.

Step 4: Segment contacts into groups

The agent can use CreateContactGroup to group contacts by context.

Example groups:

Former engineering managers
AI product collaborators
RevOps stakeholders
Implementation customers
Founder advisors

The grouping logic should remain conservative. A person who only attended the same webinar should not be grouped as a qualified endorser.

Step 5: Build a sequence template

The agent can inspect existing templates with ListSequenceTemplates and GetSequenceTemplate, then create a new one with CreateSequenceTemplate if needed.

A template might include variables from ListVariables, such as first name, project name, or skill list.

Template structure:
- Greeting
- Context
- Specific skill ask
- No-pressure close

Step 6: Launch, pause, resume, or stop

Once approved by a human, the agent can use LaunchSequence.

If a customer replies negatively, changes role, or should be excluded, the operator can use:

  • PauseSequence
  • ResumeSequence
  • StopSequence

This keeps the 80/20 model intact. Automation handles process. Humans handle judgment.

For implementation details, the platform’s MCP entry point is available at MCP tools.

Example endorsement skill maps for AI agent builders

An endorsement strategy for autonomous agent builders should reflect the actual architecture they work with. The goal is not to stuff a profile with AI terms. The goal is to make technical credibility legible.

Agent platform engineer

Useful endorsement examples:

  • Agentic Workflows
  • API Design
  • Python
  • TypeScript
  • Workflow Automation
  • Distributed Systems
  • Tool Calling
  • System Design
  • Observability
  • Cloud Architecture

Profile logic:

If the person builds the agent runtime:
System Design, Distributed Systems, API Design, Observability

If the person builds the agent application layer:
Agentic Workflows, Tool Calling, Workflow Automation, Python

LLM application engineer

Useful endorsement examples:

  • LLMOps
  • Prompt Engineering
  • Retrieval-Augmented Generation
  • Vector Databases
  • Model Evaluation
  • Python
  • Data Engineering
  • Embeddings
  • Natural Language Processing

Profile logic:

If the person ships customer-facing AI features:
LLMOps, RAG, Model Evaluation, Python

If the person focuses on knowledge systems:
Vector Databases, Embeddings, Data Engineering

RevOps automation engineer

Useful endorsement examples:

  • Revenue Operations
  • CRM Architecture
  • Workflow Automation
  • API Integrations
  • Salesforce
  • HubSpot
  • Lead Routing
  • Data Enrichment
  • Reporting Automation

Profile logic:

If the person automates GTM process:
Workflow Automation, CRM Architecture, Lead Routing

If the person maintains data quality:
Data Enrichment, Reporting Automation, API Integrations

AI solutions engineer

Useful endorsement examples:

  • Solutions Architecture
  • Technical Discovery
  • AI Automation
  • API Integrations
  • SaaS
  • Stakeholder Management
  • Security Architecture
  • Enterprise Architecture

Profile logic:

If the person works pre-sales:
Technical Discovery, Solutions Architecture, Stakeholder Management

If the person works implementation:
API Integrations, Security Architecture, Enterprise Architecture

What makes an endorsement request credible?

Credible endorsement requests have four properties.

1. The skill is observable

The endorser should have seen the skill in action. A product manager can credibly endorse an engineer for API Design if they worked through API requirements together. They may not be qualified to endorse Kubernetes internals.

2. The relationship is real

A former teammate, manager, client, or implementation partner is credible. A cold contact is not.

3. The skill list is short

Three skills are usually enough. Asking for ten skills feels transactional and noisy.

4. The request gives the person an exit

“No pressure” is not just politeness. It protects the relationship and keeps the signal honest.

A useful filter:

Would this person be comfortable explaining why they endorsed the skill?

Yes -> reasonable request
No  -> do not ask

Common mistakes in LinkedIn endorsement strategies

Mistake 1: Asking for generic endorsements

Skills like “Leadership” or “Communication” may be useful, but they are weak if they are the entire profile. Technical profiles should anchor around specific capabilities.

Better:

Weak:
Leadership, Communication, Teamwork

Stronger:
API Design, LLMOps, CRM Architecture, Workflow Automation

Mistake 2: Optimizing for volume instead of relevance

Many irrelevant endorsements can make a profile look unfocused. A smaller number of relevant endorsements from credible people is more useful.

Mistake 3: Confusing endorsements with proof

Endorsements should point toward evidence. They do not replace repositories, implementation stories, case studies, technical posts, or product demos.

Mistake 4: Automating the wrong layer

The wrong approach is trying to automate endorsement creation or manipulate the platform.

The right approach is:

Automate:
contact organization, segmentation, CSV handling, template setup, tracking

Keep human:
relationship judgment, skill accuracy, approval, sensitive exclusions

Mistake 5: Using the same ask for every contact

A former manager, customer, advisor, and peer should not receive the same request. The agent can draft variants, but the human should review the relationship context.

Cost considerations for endorsement workflow tooling

Endorsement collection can be managed manually, with a CRM, with sales engagement tooling, or with an AI agent connected to contact and sequence infrastructure.

Typical cost ranges vary by stack:

Approach Typical monthly cost range Fit
Manual spreadsheet tracking €0 to €20 Small personal network, low volume
Generic CRM plus manual outreach €20 to €150+ Consultants, founders, small teams
Sales engagement platform €50 to €300+ per user Larger GTM teams, broader sequencing
Custom internal agent workflow €200 to €2,000+ equivalent build cost Engineering teams with internal tooling
Fintalio platform plan €69 per month AI-assisted contact and sequence workflows

Fintalio has a single €69 per month plan. There is no free tier and no usage-based tiering. That makes cost planning straightforward for builders who want predictable infrastructure around contact, sequence, and hosted LinkedIn relay workflows.

The honest view: if someone needs five endorsement requests a year, a spreadsheet is enough. If a founder, consultant, or technical team wants a repeatable relationship-based workflow, structured tooling becomes useful.

A practical 80/20 operating model

A good endorsement workflow does not need to be complicated. It needs to be disciplined.

80% handled by agent
--------------------
List contacts
Parse CSV
Create contacts
Update contact metadata
Create contact groups
Inspect templates
Create templates
Launch approved sequences
Pause or stop sequences

20% handled by human
--------------------
Choose legitimate skills
Approve contact inclusion
Edit sensitive messages
Exclude weak relationships
Review replies
Protect reputation

This model is especially important for AI engineers building autonomous systems. The agent should reduce operational drag, not replace professional judgment.

A practical weekly workflow:

  1. Export or prepare a small list of relevant contacts.
  2. Parse and validate the list.
  3. Segment contacts by relationship type.
  4. Assign no more than three skills per contact group.
  5. Review the message template manually.
  6. Launch only after approval.
  7. Pause or stop when context changes.
  8. Review the profile after endorsements arrive.
  9. Adjust top skills to match desired positioning.
  10. Repeat quarterly, not constantly.

Endorsements should feel like maintenance of professional proof, not a growth hack.

FAQ

1. What are examples of LinkedIn endorsements for AI engineers?

Good examples include LLMOps, Machine Learning, Python, Retrieval-Augmented Generation, Prompt Engineering, Vector Databases, Model Evaluation, Data Engineering, and Natural Language Processing. The best skills depend on the engineer’s actual shipped work.

2. How many skills should someone ask a connection to endorse?

A practical request usually includes two or three skills. Asking for too many skills can feel unfocused and transactional. The selected skills should be ones the endorser has actually observed.

3. Can an AI agent create LinkedIn endorsements?

No. Endorsements should come from real LinkedIn members through the platform experience. An AI agent can support the surrounding workflow, such as contact grouping, CSV parsing, template preparation, and approved sequence launches.

4. Are LinkedIn endorsements useful for developers?

Yes, when they are specific and relevant. Endorsements for API Design, Python, Kubernetes, System Design, or Cloud Architecture can help make a developer’s expertise easier to understand. They should be paired with real evidence such as projects, demos, repositories, or case studies.

5. What is the best way to ask for a LinkedIn endorsement?

The best request is short, specific, and respectful. It should mention the shared project or relationship, name two or three relevant skills, and make it clear that the person should only endorse them if the skills feel accurate.

Short call to action

Fintalio helps developers and AI engineers build practical, first-party workflows around contacts, sequences, and hosted LinkedIn infrastructure. For teams that want the agent to handle the boring 80% while humans keep control of judgment, skills, and relationships, visit the site and explore the MCP tools at MCP tools.

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