Position: AI Technical Lead (TK60FT RM 4325)
Virtual Interviews on 14 & 15-Aug-26
Role Overview
We are seeking an accomplished AI Technical Lead with 6+ years of experience in designing, developing, and deploying enterprise-grade AI solutions. The ideal candidate will lead initiatives around multi-agent systems, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), fine-tuning, context engineering, and AI-driven operations (AIOps). This role requires strong technical leadership, hands-on development skills, and the ability to architect scalable AI platforms that integrate seamlessly with data pipelines and knowledge graphs (Neo4J or similar).
Key Responsibilities
- AI Architecture & Leadership
- Design and implement multi-agent AI systems for enterprise workflows.
- Develop and optimize RAG pipelines for knowledge retrieval and contextual reasoning.
- Integrate MCP for tool and API interoperability across AI agents.
- Apply context engineering to improve agent reasoning and reliability.
- Model Development
- Fine-tune LLMs and transformer-based models for domain-specific tasks and enterprise use cases.
- Evaluate trade-offs between fine-tuning, prompt engineering, and RAG for optimal performance.
- Collaborate with data scientists to design custom embeddings and model adaptations.
- Data & Knowledge Engineering
- Build and manage data pipelines for ingestion, transformation, and retrieval.
- Design and maintain knowledge graphs using Neo4J or similar graph databases to support semantic search and contextual reasoning.
- Integrate knowledge graphs with RAG pipelines for enhanced retrieval.
- Development & Deployment
- Lead development using Python (LangChain, HuggingFace, LangGraph, Google ADK, Microsoft Agent Framework etc.).
- Collaborate with front-end teams using ReactJS (nice to have) for AI dashboards and agent interaction UIs.
- Ensure scalability, observability, and reliability of AI systems using AIOps practices.
- Leadership & Collaboration
- Mentor and guide engineering teams in AI best practices.
- Collaborate with product managers, data scientists, and stakeholders to align AI solutions with business goals.
- Drive innovation by evaluating emerging AI technologies and frameworks.
Required Skills & Experience
- 6+ years of experience in enterprise AI development.
- Strong expertise in:
- Multi-agent systems and orchestration frameworks.
- RAG (Retrieval-Augmented Generation).
- MCP (Model Context Protocol).
- Fine-tuning LLMs (HuggingFace, OpenAI fine-tuning, LoRA, PEFT).
- Context Engineering for LLMs and AI agents.
- Python for AI pipelines and orchestration.
- Experience with:
- Data pipelines and ETL workflows.
- Knowledge graph creation using Neo4J or similar.
- AIOps for monitoring and deployment of AI systems.
- Exposure to ReactJS for UI development (nice to have).
- Proven track record of leading technical teams and delivering enterprise-grade AI solutions.
Preferred Qualifications
- Experience with cloud-native AI deployments (Azure).
- Knowledge of vector databases (PGVector, Pinecone, Milvus etc).
- Familiarity with DevOps/MLOps practices.
- Strong communication and leadership skills.
