AI Lead Engineer
Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on Sep 16, 2026. T-Systems scores A on the Alion truth index.
Company Introduction - T-Systems ICT India Pvt. Ltd. T-Systems Information and Communication Technology India Private Limited (T-Systems ICT India Pvt. Ltd.) is a certified Great Place To Work®, proudly recognized for its strong people-first culture and commitment to employee excellence. As a wholly owned subsidiary of T-Systems International GmbH, T-Systems India operates out of Pune and Bangalore, with a dynamic team of over 4,200 professionals delivering high-value IT services to group customers worldwide. T-Systems India plays a key role in this global vision by delivering integrated, end-to-end IT solutions and sector-specific software to drive transformation across industries, including automotive, manufacturing, logistics, transportation, healthcare, and the public sector. For over 25 years, T-Systems International GmbH has been at the forefront of digital innovation, driving progress and fostering digital optimism. As a leading European IT services provider and a proud part of Deutsche Telekom, T-Systems delivers transformative digitalization projects backed by deep expertise in consulting, cloud, AI, cybersecurity, and connectivity. With a global workforce of 26,000 employees across 26 countries, we set industry benchmarks in efficiency, sovereignty, security, and reliability-empowering organizations to unlock their full digital potential. With annual revenues exceeding EUR 4.0 billion (2024), T-Systems stands as one of Europe’s foremost digital transformation partners, committed to shaping the future of enterprise technology.
Role Overview - AI Lead Engineer (GenAI & Agentic AI)
Location - Pune / Hyderabad
Experience: 10-15+ Years (including 3-5+ years in AI/ML, Generative AI, or Agentic AI Solutions)
Role Overview
We are looking for an experienced AI Lead Engineer to drive the design, architecture, and delivery of enterprise-grade AI solutions leveraging Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. The ideal candidate will have a strong software engineering background combined with expertise in AI solution design, AI agent orchestration, cloud platforms, and modern MLOps practices.
Key Responsibilities
- Lead the design, architecture, and implementation of AI/GenAI solutions.
- Build and deploy scalable LLM-powered applications and RAG systems.
- Design and implement Agentic AI and Multi-Agent systems for business automation and intelligence.
- Define enterprise AI architecture, governance, security, and best practices.
- Collaborate with business stakeholders, product teams, and engineering teams to deliver AI solutions.
- Mentor development teams and drive AI innovation initiatives.
- Evaluate emerging AI technologies and recommend adoption strategies.
Technical Skills
- Strong expertise in AI Solution Architecture & Design.
- Strong programming skills in Python and Temporal.
- Experience with TensorFlow, PyTorch, Scikit-learn (preferred).
- Expertise in Generative AI, LLMs, RAG, Vector Databases, AI Agents, and Multi-Agent Systems.
- Hands-on experience with LangChain, LlamaIndex, Semantic Kernel, LangGraph, CrewAI, AutoGen, or similar frameworks.
- Strong understanding of Prompt Engineering, Fine-Tuning, Embeddings, and RAG Optimization.
- Experience with Azure OpenAI, OpenAI GPT Models, Claude, Gemini, Llama, Mistral, or equivalent foundation models.
- Knowledge of Model Context Protocol (MCP), Tool Calling, Function Calling, and Agent Orchestration.
- Experience with Pinecone, Qdrant, Weaviate, ChromaDB, FAISS, Azure AI Search, or similar vector databases.
- Experience with Azure AI Services, Azure OpenAI, AWS AI/ML Services, or Google Vertex AI.
- Strong understanding of MLOps/LLMOps practices using MLflow, Kubeflow, Databricks, Azure ML, etc.
- Experience with APIs, microservices, Docker, Kubernetes, and cloud-native architectures.
- Knowledge of SQL, NoSQL databases, and data engineering concepts.
- Experience with AI monitoring, observability, evaluation frameworks, and governance practices.
Preferred Qualifications
- Experience in GraphRAG, Knowledge Graphs, and Enterprise Search solutions.
- Exposure to AI governance, compliance, and Responsible AI practices.
- Experience leading AI transformation initiatives and customer-facing engagements.
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