Lead Data Science

Job Overview

At TE, we strongly believe that data and AI are strategic drivers for future success. We are building a world-class advanced analytics team that will solve some of the most complex strategic problems and deliver topline growth and operational efficiencies across our business units.

The Data and AI teams at TE are part of the TE Information Solutions (TEIS) Organization, building scalable solutions with AI agents and workflow for organization.

The Lead - Data Science will spearhead the development of intelligent agent systems and orchestrated workflows. This role focuses on designing, building, and deploying autonomous AI agents that drive business automation and operational excellence through cutting-edge Generative AI and LLM technologies.

Job Responsibilities

Agentic AI & Automation:

• Design, develop, and deploy multi-agent systems and agentic applications using frameworks like AutoGen, LangGraph, CrewAI, or similar


• Build intelligent workflow orchestration systems that enable autonomous decision-making and task execution


• Implement Agent-to-Agent (A2A) communication protocols and Model Context Protocol (MCP) for seamless agent collaboration


• Develop automation solutions using OpenAPI standards for integration with enterprise systems


• Create self-healing, adaptive workflows that optimize business processes autonomously

Generative AI & LLM Solutions:

• Use ML, deep learning, and Generative AI tools to design, evangelize, and implement state-of-the-art solutions


• Define and implement best practices for building, testing, and deploying scalable AI solutions, with a focus on generative models and LLMs using proprietary or open-source models


• Drive successful business outcomes by designing and building cloud-hosted Generative AI solutions

Technical Implementation:

• Work closely with internal teams to integrate RAG workflows, agent-based systems, and automation frameworks into applications


• Design and implement architectural solutions for Information Retrieval using RAG, Vector DBs, and Knowledge Graphs


• Work with public cloud (AWS) and on-premises infrastructure for deploying LLMs, agents, and orchestration systems


• Evaluate, build, and fine-tune ML models and LLMs to solve complex business problems


• Stay abreast of latest developments in agentic AI, autonomous systems, language models, and generative AI technologies

What your background should look like:

Education & Experience:

• BE, Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or equivalent practical experience


• 8+ years of overall technical experience with 2-3+ years of hands-on experience in Generative AI and LLM technologies


• Hands on experience building agentic systems, workflow automation, or autonomous AI applications

Agentic AI & Workflow Expertise:

• Deep hands-on experience with agentic frameworks (AutoGen, LangGraph, CrewAI, Agency Swarm, or similar)


• Strong knowledge of workflow orchestration tools and patterns (Temporal, Airflow, Prefect, or similar)


• Expertise in OpenAPI standards, Agent-to-Agent (A2A) protocols, and Model Context Protocol (MCP)


• Experience designing multi-agent architectures with memory, planning, and tool-use capabilities


• Knowledge of agent evaluation, testing frameworks, and observability patterns

LLM & Generative AI:

• Proven track record of deploying and optimizing LLM models for inference in production environments


• Extensive experience with LLM orchestration frameworks (LangChain, LlamaIndex required)


• Hands-on experience with Amazon Bedrock, SageMaker JumpStart, and other cloud-based LLM platforms


• Expertise in RAG architectures, Fine-tuning techniques, and Prompt Engineering


• Deep understanding of Vector Databases (Pinecone, Weaviate, Milvus, ChromaDB) and Knowledge Graphs

ML/DL Foundations:

• Expert in NLP techniques and deep learning libraries (Transformer models, LSTM, BiLSTM, CNN, BERT, GPT, T5)


• Proficiency with ML frameworks: TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn


• Strong programming skills in Python (required), plus JavaScript/TypeScript or Node.js


• Deep understanding of data structures, algorithms, and system design patterns

MLOps/LLMOps:

• Hands-on experience in MLOps/LLMOps including data pipelines, model training/refinement, validation, drift management, and serving


• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for ML systems


• Knowledge of monitoring, logging, and observability tools for production AI systems 

Nice to Have

• Experience with function calling, tool use, and external API integration in agent systems


• Knowledge of reinforcement learning and agent training methodologies


• Familiarity with semantic reasoning, planning algorithms (ReAct, Chain-of-Thought, Tree-of-Thoughts)


• Experience with graph databases (Neo4j, Neptune) and ontology design


• Contributions to open-source AI/ML projects


• Publications or patents in AI/ML domain

Competencies