Job Overview

TE Connectivity is seeking a hands-on Machine Learning Engineer with 3–5 years of experience to help design, productionalize, scale, and optimize AI/ML solutions across the enterprise. This role will sit at the intersection of data engineering, data science, and ML engineering, and will focus on taking models from experimentation to reliable business use in production.

The ideal candidate has strong experience with Databricks, AWS, MLflow, Python, PySpark, SQL, and modern MLOps practices. This person should be comfortable refactoring machine learning code and models, building robust production pipelines, setting up monitoring and alerts, improving model efficiency and reliability, and supporting both traditional ML models and Generative AI applications such as chatbots and agents.

Responsibilities:

• Design, build, and maintain end-to-end machine learning pipelines for training, validation, deployment, monitoring, and retraining.
• Productionalize AI/ML models and ensure they are scalable, reliable, secure, and supportable in enterprise environments.
• Refactor existing machine learning models and codebases to improve performance, maintainability, reusability, and deployment readiness.
• Develop and manage model orchestration workflows across experimentation, batch scoring, real-time inference, and retraining cycles.
• Implement MLflow-based experiment tracking, model versioning, model registry, and lifecycle management.
• Build and optimize data pipelines using PySpark, SQL, and Databricks to support feature engineering, training, and inference workloads.
• Create monitoring frameworks for model health, drift, accuracy, latency, failures, and cost, and configure automated alerts for anomalies and performance degradation.
• Improve model efficiency through better feature pipelines, compute optimization, inference tuning, and workflow redesign.
• Deploy and support Generative AI solutions, including chatbots, assistants, and agent-based workflows.
• Collaborate with data scientists, data engineers, application developers, and business stakeholders to move AI solutions from prototype to production.
• Contribute to MLOps and engineering best practices including observability, CI/CD, governance, testing, and documentation.
• Support cloud-native AI/ML solutions in AWS and help ensure alignment with security, compliance, and enterprise architecture standards.

Resposibilities:

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or related field.
• 3–5 years of hands-on experience in machine learning engineering, MLOps, data engineering, or a closely related role.
• Strong programming skills in Python.
• Strong experience with PySpark and SQL for large-scale data processing and transformation.
• Experience with Databricks for ML and data engineering workflows.
• Experience with AWS services supporting ML, data, and deployment workflows.
• Experience with MLflow for experiment tracking, model management, and lifecycle governance.
• Experience deploying machine learning models into production environments.
• Experience building monitoring, alerting, and observability processes for production models and pipelines.
• Understanding of model orchestration, model retraining workflows, and ML lifecycle management.
• Experience working across both data engineering and data science use cases.
• Strong understanding of software engineering fundamentals, including testing, version control, modular design, and code refactoring.

Preferred Qualifications

• Experience with Generative AI, LLM-based applications, chatbots, RAG, or agentic workflows.
• Experience with real-time or near-real-time inference pipelines.
• Experience with CI/CD, DevOps, or Infrastructure-as-Code in support of ML platforms.
• Experience with feature stores, model governance, and ML observability tooling.
• Experience in manufacturing, industrial, supply chain, or enterprise business environments.
• Familiarity with security, auditability, and governance requirements for enterprise AI solutions.

Key Skills

• Databricks
• AWS
• MLflow
• Python
• PySpark
• SQL
• Data Engineering
• Data Science
• Machine Learning Engineering
• MLOps
• Model Deployment
• Model Monitoring
• Alerting / Observability
• Model Refactoring
• Model Orchestration
• Generative AI
• Chatbots
• AI Agents
• Performance Optimization

What Success Looks Like

• Existing ML models are refactored into cleaner, more scalable production solutions.
• AI/ML pipelines are automated, monitored, and resilient.
• Model alerts proactively identify drift, cost anomalies, and performance degradation.
• Generative AI use cases such as chatbots and agents are deployed with strong engineering discipline.
• Model efficiency, maintainability, and time-to-production improve across TE’s AI/ML portfolio.

Competencies