Generative AI Engineer
Summary
Builds and deploys production-grade generative AI systems using LLMs, RAG, and AI agents in Python, integrating with vector databases and cloud AI platforms.
Generative AI Engineer
Experience: 4–12 Years
Location: Bengaluru
Notice Period: Immediate Joiners Only
Employment Type: Full-Time
Job Description
We are looking for a highly skilled Generative AI Engineer with hands-on experience in building and deploying production-grade GenAI solutions. The ideal candidate should have strong expertise in LLMs, RAG, Agentic AI, AI Agents, Python, prompt engineering, and vector databases.
Key Responsibilities
- Design, develop, and deploy end-to-end Generative AI solutions for enterprise use cases.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines.
- Develop LLM workflows, AI Agents, and Agentic AI solutions.
- Work with LLMs such as OpenAI/Azure OpenAI, Claude, Gemini, Llama, or equivalent models.
- Design effective prompt engineering strategies and optimize LLM responses.
- Implement document ingestion, chunking, embeddings, retrieval, reranking, and response-generation pipelines.
- Develop APIs and AI services using Python and FastAPI/Flask.
- Work with Vector Databases such as Pinecone, FAISS, Chroma, Weaviate, or Azure AI Search.
- Integrate GenAI solutions with enterprise data sources, applications, databases, and APIs.
- Implement LLM evaluation, guardrails, monitoring, security, and responsible AI practices.
- Deploy and manage GenAI applications on Azure, AWS, or GCP.
- Optimize AI solutions for accuracy, scalability, latency, reliability, and cost.
- Collaborate with business stakeholders, architects, data engineers, and AI teams to deliver production-ready solutions.
Required Skills
- 4–12 years of overall technology experience with strong hands-on experience in Generative AI.
- Strong programming expertise in Python.
- Hands-on experience with LLMs, RAG, Prompt Engineering, AI Agents, and Agentic AI.
- Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar frameworks.
- Strong understanding of embeddings, vector search, semantic search, and vector databases.
- Experience integrating LLM APIs and enterprise data sources.
- Experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent AI platforms.
- Knowledge of FastAPI/Flask, REST APIs, Docker, Git, and CI/CD.
- Experience taking GenAI solutions from POC to production.
- Strong analytical, debugging, and problem-solving skills.
Good to Have
- Experience with LLMOps/MLOps and AI observability.
- Knowledge of multi-agent architectures and advanced Agentic AI frameworks.
- Experience with fine-tuning, model evaluation, hallucination reduction, and RAG optimization.
- Understanding of AI security, data privacy, governance, and responsible AI.
Requirements
Key Skills: GenAI | Generative AI | LLM | RAG | Agentic AI | AI Agents | Python | Prompt Engineering | LangChain | LangGraph | Vector Database | Azure OpenAI | AWS Bedrock | Vertex AI | LLMOps