AI & ML

GEMBA CONCEPTS

• Experience: 3–5 years Type: Full-time
• AI/ML Engineer
• Location: Bengaluru, India (Hybrid)

About Gemba Concepts

• 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. Were a
• tight engineering team that ships real systems for demanding, often regulated, environments.

The Role

• Were looking for an AI/ML Engineer to take ML capabilities from prototype to production. Youll 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 Youll 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 Were 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 Youll 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

Skills:- Machine Learning (ML), Python, PyTorch, Convolutional Neural Network (CNN), Object Detection, Image segmentation, Time series, Anomaly Detection, Predictive Maintenance, MLOps, Kubernetes, Azure OpenAI, MLFlow, Large Language Models (LLM) tuning and Retrieval Augmented Generation (RAG)