DATA ARCHITECT

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DATA ARCHITECT

10/14/26

At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world.

Lead the architecture, design and deployment of Generative AI, Agentic AI, ML and deep learning solutions end to end: data pipelines, retrieval, agent orchestration, model serving, evaluation and monitoring on AWS Bedrock, Azure OpenAI and Databricks. Roles & Responsibility:

Solution Architecture

Own and lead the enterprise AI reference architecture and end-to-end design for Generative AI and Agentic AI solutions, covering data ingestion, retrieval, orchestration, model serving, security, and monitoring. Select the optimal models, platforms, and architectural patterns across AWS Bedrock, Azure OpenAI, and Databricks, balancing accuracy, latency, scalability, cost, and compliance requirements. Develop architecture blueprints, reference designs, and technical decision records (TDRs). Lead architecture reviews and establish engineering, security, and coding standards for AI solution delivery.

Generative AI & Agentic AI

Design and implement multi-agent systems using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, and Amazon Bedrock Agents. Build enterprise-grade RAG solutions leveraging chunking strategies, embeddings, hybrid search, semantic search, re-ranking, and vector databases. Implement prompt engineering, guardrails, structured outputs, tool/function calling, and Model Context Protocol (MCP) integrations. Establish LLMOps practices including evaluation frameworks, prompt versioning, groundedness validation, tracing, monitoring, and cost optimization.

Data Science, Machine Learning & Deep Learning

Develop machine learning and deep learning solutions for forecasting, classification, anomaly detection, optimization, NLP, and predictive analytics. Build scalable data and feature engineering pipelines using Databricks, PySpark, Delta Lake, Unity Catalog, and MLflow. Deploy AI models and intelligent agents as scalable APIs using FastAPI, Docker, Kubernetes, and CI/CD pipelines.

Qualifications Must Have

7+ years of experience in Data Science, Machine Learning, or AI Engineering, including 3+ years architecting production GenAI/LLM solutions. Proven experience designing and deploying Agentic AI or Multi-Agent systems. Hands-on experience with at least two of AWS Bedrock, Azure OpenAI, and Databricks. Strong expertise in Python, SQL, and PySpark with solid ML and statistical fundamentals. Experience deploying AI