AI / ML Engineer

We're looking for an AI/ML Engineer to take ML capabilities from prototype to production. You'll own models end-to-end, framing the problem with stakeholders, building and validating the model, and deploying it as a reliable service that holds up against real-world, messy industrial data. This is a hands-on building role, not a pure research seat: your work goes into client-facing systems.

Responsibilities:

• Build and ship computer vision models for visual quality inspection (defect detection, classification, and segmentation) that perform under real factory lighting, throughput, and edge-case conditions.
• Develop predictive maintenance models using sensor/time-series data anomaly detection, remaining-useful-life estimation, and failure prediction.
• Own the full ML lifecycle: data pipelines, feature engineering, training, evaluation, and deployment, with proper versioning and monitoring. Deploy and serve models in production on Azure (AKS), and keep them healthy, track drift, retraining triggers, and latency.
• Integrate LLM-based capabilities (we use the Claude API and self-hosted open models) into delivery and product workflows where they add leverage.
• Collaborate with product, engineering, and domain experts to translate fuzzy operational problems into well-scoped ML solutions and to know when ML is not the right answer.
• Communicate results and limitations clearly to non-ML stakeholders, including clients.

Requirements:

• 3-5 years of hands-on experience building and deploying ML models in production (not just notebooks or coursework). Strong Python and the modern ML stack: PyTorch or TensorFlow, scikit-learn, and NumPy/Pandas.
• Solid grounding in at least one of computer vision (CNNs, object detection/segmentation, image preprocessing) or time-series/anomaly detection.
• Practical MLOps experience: containerization (Docker), model serving, experiment tracking, and deploying on a cloud platform Azure / Kubernetes (AKS) is a strong plus.
• Comfort working with imperfect, real-world data labeling strategy, class imbalance, data drift, and validation that reflects production reality.
• Good engineering hygiene (Git, testing, code review) and the ability to write code others can build on.

Nice to Have:

• Experience with industrial / manufacturing data or regulated environments (pharma, 21 CFR Part 11 awareness).
• Hands-on LLM integration experience: RAG, prompt engineering, working with APIs or self-hosted models (vLLM, Qwen, etc. ).
• Edge deployment experience (running CV models on-device / near the line).
• Exposure to data pipeline tooling and orchestration.