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

Compétences requises

Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)Agentic AI / AI AgentsPrompt EngineeringPython programmingVector databases and vector search (Pinecone, FAISS, Chroma, Weaviate, Azure AI Search)LangChain / LangGraph / LlamaIndex / Semantic Kernel frameworksDocument ingestion, chunking, embeddings, retrieval and reranking pipelinesFastAPI/Flask and REST API developmentCloud deployment and productionizing GenAI (Azure, AWS, GCP)Docker, Git, and CI/CDLLM evaluation, monitoring, guardrails and responsible AI practicesLLMOps/MLOps and AI observabilityFine-tuning, model evaluation and hallucination reduction

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