Senior AI Engineer

Location: Mumbai
Experience: 10–14 Years
Employment Type: Full-Time

Role Overview

We are looking for a Senior AI Engineer with strong expertise in Knowledge Graphs, GraphRAG, LLMs, and Agentic AI to design and build next-generation enterprise AI solutions.

The role involves hands-on engineering, developing production-grade AI applications, integrating modern LLM and graph technologies, and working closely with enterprise clients, solution architects, and technical teams.

About the Role:
As a Senior AI Engineer, you will be responsible for designing, developing, and deploying AI solutions that combine Knowledge Graphs, Generative AI, RAG, and multi-agent architectures. You will work closely with solution architects, enterprise clients, data teams, and engineering teams to translate complex business requirements into scalable AI applications.

The ideal candidate should have strong software engineering fundamentals along with hands-on experience in modern AI/ML technologies. You should be comfortable working with LLMs, graph databases, vector databases, AI frameworks, cloud platforms, and production deployment environments.

Key Responsibilities

• Design, develop, and implement Knowledge Graph solutions using technologies such as Neo4j, Amazon Neptune, and TigerGraph.
• Build and optimize GraphRAG, RAG, ontology-driven, and knowledge-based AI systems.
• Develop multi-agent AI applications using frameworks such as LangGraph, LangChain, and CrewAI.
• Design and integrate LLM-powered applications using OpenAI, Claude, Llama, Gemini, Amazon Bedrock, and other foundation models.
• Develop scalable, production-grade Python backend services, APIs, and AI pipelines.
• Optimize retrieval, reasoning, context management, and overall AI workflow performance.
• Implement AI solutions with appropriate testing, CI/CD, observability, monitoring, and deployment practices.
• Work with cloud platforms including AWS, Azure, and/or GCP.
• Collaborate with enterprise clients, solution architects, data teams, and engineering teams to understand requirements and deliver scalable AI solutions.
• Stay current with emerging developments in Generative AI, Agentic AI, Knowledge Graphs, and GraphRAG.

Must-Have Skills & Experience

• 8–12 years of overall software engineering experience.
• 3+ years of hands-on experience in AI/ML engineering.
• Strong programming expertise in Python.
• Hands-on experience with Knowledge Graph technologies, preferably Neo4j, Amazon Neptune, or TigerGraph.
• Strong understanding of RAG / GraphRAG architectures.
• Experience with LangGraph, LangChain, CrewAI, or similar agentic AI frameworks.
• Experience integrating and deploying LLMs/Foundation Models, such as OpenAI, Claude, Llama, Gemini, or Bedrock.
• Experience with Vector Databases such as Pinecone, Weaviate, Qdrant, or FAISS.
• Experience building production-grade AI applications and APIs.
• Working knowledge of at least one major cloud platform: AWS, Azure, or GCP.
• Hands-on experience with Docker, CI/CD, testing, and deployment.

Required Skills & Experience

We are looking for candidates with 8–12 years of overall software engineering experience, including at least 3 years of hands-on AI/ML engineering experience.

Strong expertise in Python is essential. Candidates should have practical experience with Knowledge Graph technologies, GraphRAG/RAG architectures, LLM integration, and Agentic AI frameworks.

Experience with Neo4j, Amazon Neptune, TigerGraph, LangGraph, LangChain, CrewAI, Pinecone, Weaviate, Qdrant, or FAISS will be highly valuable.

Candidates should also have experience working with at least one major cloud platform such as AWS, Azure, or Google Cloud, along with Docker, CI/CD, testing, and production deployment.

Preferred Skills

Experience with Cypher, Gremlin, SPARQL, ontology development, schema design, semantic search, graph theory, and knowledge representation will be an advantage.

Exposure to AWS Bedrock, SageMaker, Lambda, IAM, SAP, ERP data, or enterprise data platforms will be preferred.

Candidates with contributions to AI research, open-source projects, technical publications, or innovative Generative AI solutions will also be considered positively.