GenAI Engineer:
Experience
: 3 to 12 years
Location
: Chennai/Bangalore/Hyderabad
Details on tech stack:
Programming: Advanced Python (OOP, async), REST API frameworks (Flask, FastAPI)
Cloud: Strong experience with Microsoft Azure (App Services, Azure Functions, Blob Storage, Cosmos DB preferred)
GenAI/LLM Ecosystem: Familiarity with LangChain, LangGraph, or similar orchestration frameworks Experience building solutions with RAG design patterns and prompt tuning (CoT, ToT, FewShot) Understanding of vector databases (e.g., FAISS, Pinecone, Azure Cognitive Search) Embedding models like Sentence Transformers, CLIP/SIGLIP, or similar
Performance Optimization: Hands-on experience scaling solutions for high payload volumes Token management and handling long-form data inputs
Data Integration: Ability to work with semi-structured and structured data formats, schema mapping, and transformation
Version Control & CI/CD: Git, Azure DevOps/GitHub Actions pipelines
Nice to have requirements to the candidate
Practical experience deploying GenAI applications to production in enterprise settings
Familiarity with AgentOps/MLOps pipelines
Exposure to VLLMs or lightweight open-source LLMs for enterprise deployments
Experience supporting post-go-live production systems or hypercare phases
Essential functions
GenAI Engineer:
Experience
: 3 to 12 years
Location
: Chennai/Bangalore/Hyderabad
Details on tech stack:
Programming: Advanced Python (OOP, async), REST API frameworks (Flask, FastAPI)
Cloud: Strong experience with Microsoft Azure (App Services, Azure Functions, Blob Storage, Cosmos DB preferred)
GenAI/LLM Ecosystem: Familiarity with LangChain, LangGraph, or similar orchestration frameworks Experience building solutions with RAG design patterns and prompt tuning (CoT, ToT, FewShot) Understanding of vector databases (e.g., FAISS, Pinecone, Azure Cognitive Search) Embedding models like Sentence Transformers, CLIP/SIGLIP, or similar
Performance Optimization: Hands-on experience scaling solutions for high payload volumes Token management and handling long-form data inputs
Data Integration: Ability to work with semi-structured and structured data formats, schema mapping, and transformation
Version Control & CI/CD: Git, Azure DevOps/GitHub Actions pipelines
Nice to have requirements to the candidate
Practical experience deploying GenAI applications to production in enterprise settings
Familiarity with AgentOps/MLOps pipelines
Exposure to VLLMs or lightweight open-source LLMs for enterprise deployments
Experience supporting post-go-live production systems or hypercare phases
Qualifications
Programming: Advanced Python (OOP, async), REST API frameworks (Flask, FastAPI)
Cloud: Strong experience with Microsoft Azure (App Services, Azure Functions, Blob Storage, Cosmos DB preferred)
GenAI/LLM Ecosystem: Familiarity with LangChain, LangGraph, or similar orchestration frameworks Experience building solutions with RAG design patterns and prompt tuning (CoT, ToT, FewShot) Understanding of vector databases (e.g., FAISS, Pinecone, Azure Cognitive Search) Embedding models like Sentence Transformers, CLIP/SIGLIP, or similar
Performance Optimization: Hands-on experience scaling solutions for high payload volumes Token management and handling long-form data inputs
Data Integration: Ability to work with semi-structured and structured data formats, schema mapping, and transformation
Version Control & CI/CD: Git, Azure DevOps/GitHub Actions pipelines
Would be a plus
- Practical experience deploying GenAI applications to production in enterprise settings
- Familiarity with AgentOps/MLOps pipelines
- Exposure to VLLMs or lightweight open-source LLMs for enterprise deployments
- Experience supporting post-go-live production systems or hypercare phases