Gen AI Engineer

Job Title: Generative AI Engineer

Experience:

6–15 Years Location: Bengaluru (Remote) Notice Period: Immediate Joiners Preferred Job Overview We are looking for an experienced Generative AI Engineer with a strong background in data and hands-on expertise in modern AI technologies. The ideal candidate should have previously worked as a Data Analyst, Data Engineer, Data Scientist, or Python Developer and should have successfully designed and delivered multiple Generative AI solutions. The candidate must have practical

experience

in Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI, with a proven track record of delivering at least 3–5 GenAI-based projects or products.

Key Responsibilities

Design, develop, and deploy scalable Generative AI solutions for business and enterprise use cases. Build and optimize RAG (Retrieval-Augmented Generation) pipelines using LLMs and enterprise data sources. Develop Agentic AI solutions capable of autonomous reasoning, planning, decision-making, and tool usage. Design and implement AI agents and multi-agent workflows for complex business processes. Integrate Large Language Models (LLMs) with structured and unstructured enterprise data. Work closely with Data Engineering, Data Science, and business teams to identify and develop AI use cases. Develop APIs and backend services to support GenAI applications and products. Evaluate, fine-tune, and optimize LLM performance for accuracy, relevance, latency, and scalability. Implement vector search, embeddings, and semantic retrieval solutions. Ensure GenAI applications follow best practices related to security, governance, scalability, and responsible AI. Take ownership of the end-to-end delivery of GenAI projects from solution design through production deployment. Required

Skills

6–15 years of overall IT experience. Strong previous

experience

in one or more of the following roles: Data Engineer or Data Scientist or Data Analyst Python Developer Strong hands-on programming

experience

in Python. Hands-on

experience

with Generative AI and Large Language Models (LLMs). Strong practical

experience

in: Retrieval-Augmented Generation (RAG) Agentic AI AI Agents and Multi-Agent Systems Prompt Engineering Embeddings and Semantic Search Vector Databases

Experience

with GenAI frameworks such as LangChain, LangGraph, LlamaIndex, or similar frameworks.

Experience

working with LLMs such as OpenAI GPT models, Claude, Llama, Gemini, or other open-source/commercial models.

Experience

with vector databases such as Pinecone, FAISS, Chroma, Weaviate, Milvus, or similar. Strong understanding of data processing, data pipelines, APIs, and databases.

Experience

deploying AI/ML or GenAI applications into production environments. Knowledge of cloud AI platforms such as AWS Bedrock, Azure OpenAI, Google Vertex AI, or similar is preferred. Mandatory

Experience

Must have successfully delivered at least 3–5 Generative AI-based projects or products. Demonstrated hands-on

experience

in building and deploying RAG-based applications. Demonstrated hands-on

experience

with Agentic AI or AI Agent workflows. Strong data background with previous

experience

in Data Engineering, Data Science, Data Analytics, or Python Development.

practical

experience

in Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI, with a proven track record of delivering at least 3–5 GenAI-based projects or products.

Key Responsibilities

Design, develop, and deploy scalable Generative AI solutions for business and enterprise use cases. Build and optimize RAG (Retrieval-Augmented Generation) pipelines using LLMs and enterprise data sources. Develop Agentic AI solutions capable of autonomous reasoning, planning, decision-making, and tool usage. Design and implement AI agents and multi-agent workflows for complex business processes. Integrate Large Language Models (LLMs) with structured and unstructured enterprise data. Work closely with Data Engineering, Data Science, and business teams to identify and develop AI use cases. Develop APIs and backend services to support GenAI applications and products. Evaluate, fine-tune, and optimize LLM performance for accuracy, relevance, latency, and scalability. Implement vector search, embeddings, and semantic retrieval solutions. Ensure GenAI applications follow best practices related to security, governance, scalability, and responsible AI. Take ownership of the end-to-end delivery of GenAI projects from solution design through production deployment. Required

Skills

6–15 years of overall IT experience. Strong previous

experience

in one or more of the following roles: Data Engineer or Data Scientist or Data Analyst Python Developer Strong hands-on programming

experience

in Python. Hands-on

experience

with Generative AI and Large Language Models (LLMs). Strong practical

experience

in: Retrieval-Augmented Generation (RAG) Agentic AI AI Agents and Multi-Agent Systems Prompt Engineering Embeddings and Semantic Search Vector Databases

Experience