AI/ML Engineer

• Gemba Concepts is a lean manufacturing and technology consulting firm helping clients across pharma, manufacturing, and logistics modernize how they operate. We build production systems that sit close to the shop floor — warehouse management, manufacturing traceability, and an applied AI/ML platform whose flagship use cases are visual quality inspection and predictive maintenance. We're a tight engineering team that ships real systems for demanding, often regulated, environments

The Role

• 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.

What You'll Do

• Build and ship computer vision models for visual quality inspection (defect detection, classification, 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, 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.

What We're Looking For

• 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, 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.

What You'll Get

• Real ownership of ML systems that go into production for serious clients.
• A lean, senior-heavy team where you ship fast and learn across the stack.
• Direct exposure to applied AI in manufacturing — a domain where the work has tangible, physical impact.

Interested Candidate please share your resume at: [Confidential Information]