I Analyzed 313 AI Engineer Job Listings, Here's Exactly What Companies Want in 2026

I scraped and analyzed 313 unique AI engineering job listings from LinkedIn across India. No fluff, no guessing. Just raw data on what skills, tools, and experience levels companies are actually hiring for right now.

Here's everything I found.

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The Dataset

• 313 unique jobs (deduplicated from 725 raw listings)
• AI engineer roles across India

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1. These Jobs Are Fresh — The Market Is Hot

Most of these listings were posted within the last week. This isn't stale data from months ago.

Over half the jobs were posted in the last 7 days. If you're job hunting, apply fast — competition moves quickly.

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2. Where Are the Jobs? (Spoiler: Bengaluru)

Bengaluru absolutely dominates with 81 jobs (26%), more than the next three cities combined. The Indian AI engineering market is concentrated in a handful of tech hubs.

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3. Work Type: Remote Is Rare

Only 3% of roles are remote. If you're counting on working from home, the Indian AI job market hasn't caught up yet. Most listings don't specify work type, but on-site and hybrid make up the bulk of explicit mentions.

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4. Competition Is Fierce

The median applicant count is 100 per role. Some popular listings had 200+. This is a competitive market — your resume and skills need to be sharp.

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5. What Roles Are Companies Actually Hiring For?

"AI Engineer" is the catch-all title, but the actual role breakdown reveals where companies are investing:

GenAI and Agentic AI roles together account for 33% of all listings — this is the fastest-growing segment.

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6. Seniority: Most Roles Are Mid-Level

Only 12% of roles are explicitly Senior, and a tiny 2% are Junior/Entry. The bulk (79%) are mid-level — companies want people who can hit the ground running, not fresh graduates, but they're not requiring 10 years of experience either.

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7. Python Is King — By a Massive Margin

This one isn't close. Python appears in 83% of job listings. Everything else is secondary. SQL and JavaScript are useful extras, but if you don't know Python, you're locked out of most of these roles.

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8. Frameworks: LangChain for Agents, PyTorch for Models

LangChain leads by a wide margin because the market has shifted heavily toward agentic AI and RAG pipelines. PyTorch and TensorFlow remain strong for model training work. If you're aiming for GenAI roles specifically, LangChain + FastAPI is the core stack.

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9. LLM Providers: OpenAI Still Dominates

Despite all the competition, OpenAI / GPT appears in 34% of listings — almost double Claude's 18%. Companies are standardizing on the OpenAI API as the default, with Claude as the strong second choice.

Note: Azure OpenAI is counted separately here — if you combine OpenAI + Azure OpenAI, the gap widens further. Also, many jobs list multiple providers, so these overlap.

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10. Cloud: Azure Edges Out AWS

All three major clouds are in demand — and the percentages are close enough that you should treat all three as important. Azure's slight edge likely comes from its tight integration with OpenAI and Microsoft's enterprise AI push.

If you're just starting out and need to pick one, Azure is currently the most in-demand for AI roles specifically.

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11. Key AI Concepts: RAG Is at the Peak

RAG (Retrieval-Augmented Generation) appears in 48% of all job listings — nearly 1 in 2 roles. Agentic AI is right behind it. These two concepts together define what the market calls "GenAI engineering" right now.

If you only have time to learn two things, make it RAG and Agentic AI.

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12. Vector Databases: All Four Are Viable

For RAG, you need a vector database. The market is still fragmented across four main options, with Pinecone leading but none having a dominant share.

Practically, they're interchangeable at the API level. Pick one, learn it well, and mention it on your resume. Pinecone is the safest choice if you want the most job matches.

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13. DevOps Skills Matter More Than You Think

AI engineers are expected to ship and maintain systems, not just prototype. CI/CD and Docker appear in ~30% of listings — these aren't optional extras.

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14. Experience Requirements

Only 62% of listings explicitly stated experience requirements. Of those:

• Median: 3 years
• Mean: 4.9 years (pulled up by senior and lead roles)
• Most common requirement: 3 years (49 jobs), followed by 2 years (37 jobs)
• Senior roles: median 4 years
• Lead / Principal roles: median 8 years

The sweet spot is 2–4 years of Python + ML/AI experience. Don't be discouraged if you have 2 years — a large chunk of these roles are within reach.

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15. Education: Bachelor's Is the Baseline

A Bachelor's in CS, engineering, or a related field is the expected baseline. Master's degrees are a meaningful differentiator but not required for most roles. PhDs are a niche requirement, typically for research-heavy positions.

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16. Top Hiring Companies

The market is highly fragmented — 197 of 234 companies had just 1 listing. There's no single dominant employer. Uplers, LTIMindtree, and EXL lead the volume.

The fragmentation is actually good news — opportunities are spread across many companies, not locked up in a few.

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17. Skills That Always Come Together

The co-occurrence analysis reveals the skill clusters that companies treat as a package deal:

Python is the common thread in every top pair. LangChain + RAG appearing together in 100 jobs confirms that agentic RAG pipelines are the dominant architecture companies are building.

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18. Skills Demand by City

Skill demand is fairly consistent across cities, but Bengaluru shows the strongest concentration across every category — which tracks with it having the most jobs overall. Hyderabad and Pune are strong on cloud and framework skills.

Dataset: 313 unique AI engineering jobs scraped from LinkedIn, India, 2026. Analysis performed with Python + pandas + matplotlib.

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Resume Guide: How to Position Yourself for These Roles

Must-Have Keywords

Tier 1 — Mentioned in 30%+ of listings (put these front and center)

Tier 2 — Mentioned in 15–30% of listings (include where relevant)

TensorFlow, Git/GitHub, Kubernetes, Hugging Face, FastAPI, SQL, JavaScript/TypeScript, Pinecone, NLP, Anthropic/Claude, Transformers, Meta Llama, LlamaIndex

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Skills Section Template

Structure your skills section in categories that mirror what ATS and recruiters scan for:

Languages:        Python, SQL, JavaScript/TypeScript, Java
AI/ML Frameworks: LangChain, PyTorch, TensorFlow, Hugging Face, LlamaIndex, Scikit-learn
LLM & GenAI:      OpenAI API, Anthropic Claude, Llama, Mistral, Prompt Engineering, Fine-tuning
AI Concepts:      RAG, Agentic AI, Embeddings, Transformers, NLP, Multi-Agent Systems
Vector Databases: Pinecone, FAISS, Weaviate, ChromaDB
Cloud:            AWS (SageMaker, Bedrock), Azure (OpenAI, Cognitive Services), GCP (Vertex AI)
DevOps:           Docker, Kubernetes, CI/CD, Git, MLflow
Databases:        PostgreSQL, MongoDB, Redis
Web Frameworks:   FastAPI, Flask, Streamlit

Note: Only include skills you can actually speak to in an interview.

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Experience Bullets: What to Emphasize

Based on the most common responsibilities in job descriptions:

RAG pipelines (48% of jobs)

Designed and deployed a Retrieval-Augmented Generation pipeline using LangChain, Pinecone, and OpenAI embeddings, reducing hallucination rate by X% across enterprise knowledge base queries.

Agentic AI (43% of jobs)

Built multi-agent orchestration system using LangGraph/CrewAI enabling autonomous task planning, tool use, and API integration across internal services.

Fine-tuning (37% of jobs)

Fine-tuned Llama-2 7B on domain-specific dataset of X records using LoRA/QLoRA, achieving X% improvement on task-specific benchmarks over base model.

Prompt Engineering (33% of jobs)

Developed and optimized prompt chains with structured output parsing, context management, and guardrails for production LLM applications serving X users.

Cloud deployment (45–49% of jobs)

Deployed ML inference endpoints on AWS SageMaker / Azure ML with auto-scaling, monitoring, and CI/CD pipelines using Docker and GitHub Actions.

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Tailoring for Role Categories

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Experience Requirements Benchmark

Median across all listings: 3 years. If you have 2+ years of relevant experience, you qualify for the majority of postings.

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Education

• Bachelor's is mentioned in 62% of listings — this is the baseline
• Master's in 28% — helpful but not required for most roles
• PhD in only 3% — rarely required outside research roles
• Certifications (AWS AI, Azure AI, Google Cloud) can supplement if your degree isn't in CS/AI

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ATS Formatting Checklist

• Single-column layout, no tables or text boxes
• Standard fonts (Arial, Calibri, Times New Roman)
• No images, icons, or graphics
• Section headings: Experience, Skills, Education, Projects
• File name: FirstName_LastName_AI_Engineer_Resume.pdf
• Dates in standard format: Jan 2023 – Present
• Spell out acronyms at least once: Natural Language Processing (NLP)
• Match job title keywords from the posting in your headline/summary
• Keep to 1–2 pages

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Quick Wins

• Mirror the job posting. If it says "Retrieval-Augmented Generation", use that exact phrase, not just "RAG"
• Lead with Python. It's in 83% of listings — make sure it's visible in the first third of your resume
• Name your cloud platforms. Don't just say "cloud experience" — say AWS, Azure, or GCP explicitly
• Quantify everything. "Reduced latency by 40%", "Processed 10M documents", "Served 5K daily users"
• Include a Projects section. If you've built RAG apps, agents, or fine-tuned models — even personal projects — list them with the tech stack
• Use both the acronym and full form. Write "Large Language Models (LLMs)" so ATS catches both variants