Senior AI & Machine Learning Engineer

Role Overview

The Senior AI & Machine Learning Engineer will play a leading role in driving advanced AI and machine learning solutions across a high-tech industrial ecosystem. This role focuses on designing, deploying, and scaling production-grade ML models and Generative AI systems to optimize operational efficiency, predictive capabilities, and data-driven decision-making.

Working closely with domain experts and engineering teams, the specialist will turn complex industrial data into high-impact operational tools.

Key Responsibilities

Machine Learning & Predictive Analytics

• Deploy production ML models for yield analytics, defect attribution, anomaly detection, predictive maintenance, and vision inspection.
• Work fluently across structured time-series sensor data, operational event logs, and unstructured industrial datasets (images, documentation, text).
• Evaluate model success against tangible business and operational KPIs (e.g., yield uplift, false-alarm reduction, cost of defect avoided).

Generative AI & Agentic Workflows

• Design RAG (Retrieval-Augmented Generation) architectures across enterprise technical documentation (SOPs, manuals, incident logs) utilizing hybrid retrieval strategies and domain-tuned embeddings.
• Implement agentic workflows, robust evaluation frameworks (faithfulness/citation metrics), and governance guardrails.

MLOps & Production Engineering

• Build end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, continuous monitoring, and automated retraining.
• Manage containerized deployments (Docker/Kubernetes), CI/CD pipelines, model registries, and feature stores.
• Execute shadow/champion-challenger deployments and monitor for data, concept, and infrastructure drift in regulated operational environments.

Data Science & Cross-Functional Collaboration

• Frame complex operational challenges into clear analytical and predictive problems using causal, experimental, or observational approaches.
• Partner directly with subject matter experts to curate datasets and establish labeling workflows.

Skills & Experience Required

• Education: Bachelor's or Master's in Computer Science, Data Science, Engineering, Physics, Applied Mathematics, or equivalent practical experience.
• Experience: 5+ years of hands-on experience in ML engineering or applied data science with direct production ownership.
• Core Tech Stack: Proficiency in PyTorch, TensorFlow, and Scikit-Learn.
• Cloud & MLOps: Production experience with model registries, containerization, and cloud deployment across major platforms (AWS, Azure, or GCP).
• Domain Experience: Proven track record deploying ML models into industrial, manufacturing, or IoT environments (interfacing with SCADA, MES, historians, or sensor networks).
• GenAI Expertise: Practical experience deploying production GenAI systems (RAG pipelines, embedding fine-tuning, evaluation harnesses, or agentic frameworks).
• Production Ownership: Strong track record of transitioning models from notebook exploration to live production, including monitoring, incident response, and lineage tracking.
• Communication: Ability to collaborate seamlessly with non-technical business and operational stakeholders.

Preferred Qualifications

• Experience with Computer Vision for quality/defect inspection (segmentation, classification).
• Exposure to digital twin/process simulation, Reinforcement Learning, or Bayesian optimization in process control.
• Experience deploying models to edge/on-device hardware.
• Cross-industry exposure (e.g., MedTech, Pharma, Process, or Discrete Manufacturing).