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From 51 similar postings that came down. Coming down is not always a hire.
Senior MLOps Technical Lead (IN)
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDesign and build RAG and agentic AI solutions from prototype to production
Build retrieval pipelines covering ingestion, embeddings, indexing, reranking and citations
Build backend services and APIs with FastAPI and containerized deployment
Build and maintain data and AI pipelines on Databricks
Evaluate solution quality with retrieval metrics, RAG evaluations and human review
From the employer’s posting
Design and build RAG and agentic AI solutions from prototype through production
Develop retrieval pipelines covering ingestion, embeddings, indexing, retrieval, reranking, grounding, and citations
Build backend services and APIs using FastAPI, REST APIs, and containerized deployment patterns
Build and maintain data and AI pipelines using Databricks
Evaluate solution quality using retrieval metrics, RAG evaluations, LLM-based evaluation, and human review
Posting record
Report an errorActively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 12, 20261 day ago
- Recorded sightings
- 2
- Last seen by us
- Nov 13, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
HCL Technologies is hiring
357 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- 1% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Azure
- Databricks
- Docker
- FastAPI
Where the posting names them
· Design and build RAG and agentic AI solutions from prototype through production · Develop AI workflows using Python, LangGraph, LangChain, or comparable frameworks · Build backend services and APIs using FastAPI, REST APIs, and containerized deployment patterns
3+ years of relevant software, data, machine learning, or AI engineering experience · Strong programming capability in Python and experience building production APIs or backend services · Hands-on experience with RAG, LLM applications, or agentic AI workflows
· Experience with Microsoft Azure and cloud-based application deployment · Familiarity with Databricks, data pipelines, SQL, and common Python data/ML libraries · Understanding of authentication, authorization, RBAC, secrets management, logging, and monitoring
· Build and maintain data and AI pipelines using Databricks · Integrate AI applications with Azure and enterprise services, including identity, security, logging, and monitoring · Evaluate solution quality using retrieval metrics, RAG evaluations, LLM-based evaluation, and human review
· Hands-on experience with RAG, LLM applications, or agentic AI workflows · Experience with FastAPI, REST APIs, Docker, Azure DevOps Git, and CI/CD · Experience with Microsoft Azure and cloud-based application deployment
· Experience with FastAPI, REST APIs, Docker, Azure DevOps Git, and CI/CD · Experience with Microsoft Azure and cloud-based application deployment · Familiarity with Databricks, data pipelines, SQL, and common Python data/ML libraries
Other Requirements Experience with GraphRAG, knowledge graphs, ontologies, graph databases, Azure AI services, MCP, or enterprise technology environments.
· Support GraphRAG and knowledge graph use cases that combine structured and unstructured information · Build and maintain data and AI pipelines using Databricks · Integrate AI applications with Azure and enterprise services, including identity, security, logging, and monitoring
· Develop AI workflows using Python, LangGraph, LangChain, or comparable frameworks · Build backend services and APIs using FastAPI, REST APIs, and containerized deployment patterns · Develop retrieval pipelines covering ingestion, embeddings, indexing, retrieval, reranking, grounding, and citations
Your next step
Complete your application on careers.hcltech.com. The employer’s form will show what is required.
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Has HCL Technologies posted this job before?
Yes. We’ve followed 6 other Senior MLOps Technical Lead (IN) jobs there since November 2026. All 6 are still up.
Large employers often post one title for several teams, so these can be separate openings.
About HCL Technologies
- People
- About 20,206 in 2024.Benefit plan filings (Form 5500)
- Company
- Listed as HCLTECH.NS.
- H-1B visas
- 754 approved for new hires in fiscal 2025.US Citizenship and Immigration Services
- Layoff notices
- Notices this year and last cover 120 people. The latest was filed March 27, 2026.State WARN notices
Demand for AI Engineer roles
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
Job description
Job Summary
The team is seeking an AI/ML Engineer to build and operationalize AI solutions across RAG, agentic workflows, knowledge graphs, and machine learning. The role is hands-on and spans experimentation, backend development, deployment, evaluation, and production support.
Key Responsibilities
· Design and build RAG and agentic AI solutions from prototype through production
· Develop AI workflows using Python, LangGraph, LangChain, or comparable frameworks
· Build backend services and APIs using FastAPI, REST APIs, and containerized deployment patterns
· Develop retrieval pipelines covering ingestion, embeddings, indexing, retrieval, reranking, grounding, and citations
· Support GraphRAG and knowledge graph use cases that combine structured and unstructured information
· Build and maintain data and AI pipelines using Databricks
· Integrate AI applications with Azure and enterprise services, including identity, security, logging, and monitoring
· Evaluate solution quality using retrieval metrics, RAG evaluations, LLM-based evaluation, and human review
· Collaborate with engineers, architects, product teams, and domain specialists to move use cases into production
· Support technical documentation, troubleshooting, optimization, and post-production improvements
Skill Requirements
3+ years of relevant software, data, machine learning, or AI engineering experience
· Strong programming capability in Python and experience building production APIs or backend services
· Hands-on experience with RAG, LLM applications, or agentic AI workflows
· Experience with FastAPI, REST APIs, Docker, Azure DevOps Git, and CI/CD
· Experience with Microsoft Azure and cloud-based application deployment
· Familiarity with Databricks, data pipelines, SQL, and common Python data/ML libraries
· Understanding of authentication, authorization, RBAC, secrets management, logging, and monitoring
· Experience evaluating AI systems through retrieval metrics, RAGAS or similar frameworks, or task-specific benchmarks
· Strong communication and collaboration skills across technical and non-technical teams
· Ability to take ownership and work iteratively from experimentation to secure, maintainable production solutions
Other Requirements
Experience with GraphRAG, knowledge graphs, ontologies, graph databases, Azure AI services, MCP, or enterprise technology environments.
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
India
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- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.