AI / Gen AI Engineer/Architect

Job Description

Title: AI / GenAI Engineer/Architect (KG, GraphDB, GraphRAG)

Location: Primary: Hyderabad, Mumbai,Pune

Employment Type: Full-time, Hybrid

Experience : 4-15 yrs

We help enterprises transform fragmented data, systems, documents, and business knowledge into trusted, governed, reusable, and AI-ready enterprise context. Our work spans modern data foundations, enterprise data architecture, integration, semantic layers, knowledge graphs, GraphRAG, ontologies, metadata, intelligent automation, and production-grade AI systems.

We believe AI does not scale on data alone. It scales on trusted context.

Context66 combines deep architecture expertise, hands-on engineering, AI-native delivery, and reusable accelerators to help organizations simplify complexity, modernize platforms, improve data trust, and move AI from experimentation into measurable enterprise outcomes.

Job Description
We are seeking a visionary AI/GenAI Engineer/Architect to lead the design and delivery of production-grade AI solutions for enterprise clients. You will architect LLM and RAG pipelines, design multi-agent AI systems, oversee the full ML lifecycle, and embed AI governance patterns - ensuring our clients move from AI experimentation to scalable, trusted, production AI. Experience with knowledge graphs and GraphRAG patterns is a strong differentiator in this role. You will deliver AI solutions across Financial Services, Healthcare, Retail, Manufacturing, Technology, Energy, Media, Government, Education, and Hospitality clients, with a particular focus on our Growth & Revenue AI and Finance AI capability areas.

Key Responsibilities

• Architect end-to-end GenAI solutions including LLM selection, RAG pipeline design, prompt engineering frameworks, and retrieval optimization
• Design multi-agent AI systems with role specialization, task decomposition, orchestration, and memory management
• Define ML lifecycle architecture covering model development, evaluation, deployment, monitoring, and retraining pipelines
• Integrate knowledge graph and graph database patterns into GenAI solutions including GraphRAG retrieval architectures
• Establish AI governance frameworks covering model explainability, bias detection, responsible AI policies, and audit trails
• Design AI solutions aligned to our capability areas - Growth & Revenue AI (personalization, demand forecasting, customer intelligence), Finance AI (risk modeling, forecasting, compliance automation), and Legal & HR Next (contract intelligence, workforce analytics)
• Design vector store architectures, embedding pipelines, and hybrid retrieval systems for enterprise-scale AI workloads
• Develop AI solution reference architectures and reusable accelerators for rapid client deployment
• Collaborate with data and enterprise architects to ensure AI architectures are grounded in governed, trusted data foundations

Soft Skills & Culture Fit
Visionary, intellectually curious, pragmatic, client-focused, ethical, cross-functional collaborator, industry-aware

Industry & Domain Exposure
Candidates should have working experience or demonstrable exposure across 2 or more of our served industries: Financial Services, Healthcare & Life Sciences, Retail & Consumer Goods, Manufacturing & Supply Chain, Technology & Telco, Energy & Utilities, Media & Entertainment, Government & Public Sector, Education, or Hospitality & Travel. Familiarity with at least one of our core capability areas - Modern Supply Chain & Operations, Finance AI, Growth & Revenue AI, Legal & HR Next, or Enterprise Office Transformation - is strongly preferred.

Technical Requirements

Must-Have Skills

• Tier 1: LLM/GenAI solution architecture, RAG pipeline design, Python for AI/ML, cloud AI/ML platforms (AWS/Azure/GCP), MLOps and model lifecycle management, REST and event-driven API design for AI services, Feature Store enablement and integrations
• Tier 2: Vector database architecture, multi-agent orchestration frameworks, knowledge graph integration with LLMs, GraphRAG patterns, AI governance and responsible AI frameworks, embedding model evaluation, Data Modeling, Ontologies, Data Catalogs, etc.

Nice-to-Have Skills
Fine-tuning and RLHF experience, agentic security (human-in-the-loop controls), AI observability and drift detection, ontology modeling, semantic search architecture