GenAI Technical Lead

Open Hall, NL
On-site
Posted Oct 15, 2026
1 position

Opens LinkedIn

Employment type: Full-time
Experience level: Lead · 10+ years
Posting language: English
Working hours: 40 hours per week
Seniority: Mid-Senior level
Application method: Direct apply is available

Job summary

Design and implement production-grade Generative AI workflows, agents, RAG pipelines, and cloud-based microservices integrated with enterprise systems. Lead and mentor a small engineering team, establish development best practices, and collaborate with stakeholders to translate business needs into technical solutions.

Job details

GenAI Technical Lead Experience Level: Minimum 7-10 years of total IT experience (at least 2+ years in Generative AI, LLMs, or related AI/ML technologies) Should have strong technical expertise in Python, hands-on experience with at least one GenAI framework (LangGraph, LangChain, or Google AI Development Kit), and strong working knowledge of one hyperscaler platform (Google Cloud, Azure, or AWS). The associate should lead solution design, integrating LLMs into enterprise workflows, mentoring team members, and driving production-grade implementation of GenAI use cases. Good knowledge of MLOps or DevOps teams to automate model deployment, versioning, and monitoring. Key Responsibilities: 1. Solution Design Design, architect, and implement Generative AI workflows and agents using frameworks such as LangChain, LangGraph, or Google ADK. Integrate LLMs (e.g., Llama, Gemini, GPT, Claude) into enterprise systems and custom applications. Define and implement retrieval-augmented generation (RAG) pipelines using vector databases (e.g., ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine). Architect scalable and secure GenAI microservices leveraging cloud-native components. 2. Development & Implementation Lead Python-based development efforts for building prompt orchestration, tool agents, and data pipelines. Develop and deploy APIs or microservices integrating LLMs with enterprise data sources. Implement prompt optimization, context management, and model performance tuning. 3. Cloud Integration Architect, deploy, and monitor GenAI workloads on one hyperscaler: GCP (Vertex AI, Document AI, AlloyDB, BigQuery, Cloud Run) Azure (OpenAI Service, Cognitive Search, Azure ML) AWS (Bedrock, SageMaker, Lambda, API Gateway) Manage cloud infrastructure for scaling AI models, ensuring cost efficiency and compliance. 4. Leadership & Mentoring Lead a small team of AI engineers and developers. Conduct code reviews, enforce best practices, and mentor junior engineers. Collaborate closely with product owners, data engineers, and business stakeholders to translate business needs into technical requirements. Contribute to internal GenAI capability building and reusable assets for the organization. Required Skills & Experience: Core Technical Skills Python (advanced proficiency; ability to build APIs, pipelines, and modular frameworks). Hands-on with at least one GenAI framework: LangChain, LangGraph, or Google ADK (AI Development Kit). Expertise with LLM integration (OpenAI API, Gemini API, Ollama, Hugging Face, etc.). Experience with RAG, embeddings, and vector databases. Familiarity with PEFT, LoRA, or prompt fine-tuning approaches. Cloud / Hyperscaler Expertise (at least one required) Google Cloud Platform (GCP) – Vertex AI, Document AI, BigQuery, AlloyDB, Cloud Run, IAM Azure – Azure OpenAI, Cognitive Search, Azure ML, Azure Functions AWS – Bedrock, SageMaker, Lambda, API Gateway, DynamoDB Other Desirable Skills Knowledge of REST APIs, JSON, and FastAPI/Flask frameworks. Familiarity with data governance, PII handling, and AI ethics principles. Understanding of Docker/Kubernetes, CI/CD, and Git-based version control. Exposure to front-end integration with AI chat agents (React, Streamlit, Gradio, etc.) is a plus. Location: Offshore, open to all TCS ODC located areas

What you’ll do

Requirements

Requires 7–10 years of total IT experience, including at least 2 years in Generative AI, LLMs, or related AI/ML technologies, along with advanced Python skills and hands-on experience with a GenAI framework. Candidates must have working expertise in at least one major cloud platform and experience with LLM integration, RAG, embeddings, and vector databases; MLOps/DevOps knowledge and team leadership are also expected.