Senior AI Engineer

JOB DESCRIPTION

Experience : 6 - 8 Yrs

Location : Bengaluru

Designation : Senior AI Engineer

Key Responsibilities

• Build and maintain RAG pipelines — document ingestion, chunking, embedding, vector retrieval, and response generation — using pre-trained LLMs and vector databases
• Develop agentic AI workflows and task automation using LangChain, LangGraph, or similar orchestration frameworks
• Integrate LLM APIs (OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI) into product features, internal tools, and customer-facing applications
• Design and maintain prompt engineering strategies — prompt templates, few-shot examples, output parsers, and guardrails
• Build backend AI APIs and microservices that expose AI capabilities to other teams and systems Connect AI systems to enterprise data sources — databases, document stores, APIs, and thirdparty SaaS platforms
• Deploy AI services on AWS / GCP / Azure using Docker and managed cloud services
• Monitor and evaluate AI outputs — track accuracy, hallucinations, latency, and cost, and iterate accordingly
• Collaborate with product managers, designers, and backend engineers to ship AI-powered features
• Maintain documentation for AI integrations, prompt libraries, and retrieval configurations

Required Skills & Qualifications

• 5+ years of software engineering experience with 3+ years building AI-powered applications
• Strong proficiency in Python — API development, data processing, async patterns
• Hands-on experience building RAG systems using tools like LangChain, LlamaIndex, or custom pipelines
• Practical knowledge of LLM APIs — prompt design, context management, function calling, and output structuring
• Experience with at least one vector database (Pinecone, Weaviate, Chroma, FAISS, or Qdrant)
• Familiarity with LangChain / LangGraph or similar agentic frameworks
• Working knowledge of AWS, GCP, or Azure — deploying containerized services and using managed AI/ML services
• Solid understanding of REST API design and backend integration patterns
• Comfortable with Git, CI/CD pipelines, and basic DevOps practices