Genai Lead / Genai Engineer / Forward Deployed Engineer (fde)

About the Role

Design, build, and deploy enterprise-grade Generative AI solutions (LLMs, RAG, agentic AI) and take them from rapid prototyping to production. Work closely with engineering teams, business stakeholders, and customers to identify use cases, implement scalable Python-based AI applications, and establish MLOps/LLMOps and cloud architectures.

Fast prototyping and productionizing GenAI solutions with LLMs, LangChain, RAG and vector DBs — emphasizes rapid AI-driven development and deployment.

Location: Chennai

Type: Full Time, Permanent

Posted: 3 days ago

Category: AI/ML & Data

Vibe Markers: 🔥 Hot

Experience Level: Senior

Job Description

Role

Experienced Generative AI professional role to design, develop, architect, and deploy enterprise-grade GenAI solutions (LLMs, RAG, Agentic AI). Candidates will work across prototyping, production deployment, and customer-facing forward-deployed engineering engagements.

Location & Employment

• Locations: Chennai / Bangalore / Hyderabad
• Employment type: Full Time

Key Responsibilities

• Design, build, architect, and integrate Generative AI and LLM-based solutions using Python and modern AI frameworks.
• Develop enterprise-grade RAG pipelines: document ingestion, chunking, embeddings, retrieval, reranking, context management, and LLM response generation.
• Design and implement Agentic AI solutions including agents, multi-agent workflows, orchestration, tool/function calling, MCP, memory, and human-in-the-loop capabilities.
• Build scalable, production-ready GenAI/AI-ML applications and services; implement microservices, APIs, and event-driven systems.
• Work with LangChain or similar frameworks, vector databases, semantic search, knowledge graphs, GraphRAG, and intelligent document processing.
• Design, deploy, and scale AI solutions on cloud platforms (Azure, AWS, GCP) and use cloud AI/ML services, Kubernetes, CI/CD, and observability tools.
• Implement MLOps / LLMOps: model lifecycle, deployment, monitoring, governance, explainability, and production readiness.
• Collaborate with product, engineering, data, and business teams to translate problems into scalable AI solutions; engage with customers and stakeholders; mentor engineers and define technical standards.

Required Technical Skills

• Generative AI, LLMs, Prompt Engineering, RAG, Advanced RAG
• Agentic AI, AI Agents, Multi-Agent Systems, Tool Calling / Function Calling, MCP, LLM orchestration
• Vector databases, embeddings, semantic search, reranking, Knowledge Graphs, GraphRAG
• Intelligent document processing and document ingestion pipelines
• Strong Python programming; REST APIs and backend development; full-stack AI application development
• React and/or Angular for front-end interfaces
• Microservices, cloud-native application development
• Microsoft Azure and/or AWS and/or GCP; Azure AI/ML services
• Kubernetes, CI/CD, MLOps / LLMOps, model deployment, monitoring, governance, observability

Preferred Experience

• Experience in regulated domains (finance, banking, healthcare) or working directly with customers in consulting/forward-deployed engineering contexts
• Experience defining AI strategy, roadmaps, reference architectures, and technical standards
• Strong stakeholder management, communication, and ability to own initiatives end-to-end

Experience Levels

• Forward Deployed Engineer: 6 - 10 years
• GenAI Engineer: 6+ years
• GenAI Lead: 10+ years

Key Competencies

Generative AI & LLMs, RAG/GraphRAG, Agentic AI, Python, Cloud AI, Vector Databases, Knowledge Graphs, MLOps/LLMOps, Data Architecture, Full-Stack Development, Solution Architecture, Customer/Stakeholder Management, Technical Leadership, Problem Solving

Tech Stack

Python, LangChain, React, Angular, Microsoft Azure, AWS, GCP, Azure AI/ML services, Kubernetes, CI/CD, REST APIs, Vector Databases, GraphRAG, Knowledge Graphs, Embeddings, Semantic Search, LLMs, Tool Calling, Function Calling, MCP

Skills

Generative AI & LLMs, Prompt Engineering, RAG, Agentic AI, Solution Architecture, MLOps / LLMOps, Cloud Architecture, Full-stack Development, Python Programming, REST APIs, Microservices, Vector Databases, Semantic Search, Knowledge Graphs, Data Architecture, Model Deployment & Monitoring, Stakeholder Management, Technical Leadership, Prototyping, Customer Engagement, Problem Solving