SR. DATA SCIENTIST
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Data science
- Posted
Skills
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
Full description
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 Senior Data Scientist - Agentic AI & Workflow Automation 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.
ResponsibilitiesAgentic 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