AI/ML ENGINEER

Job Description AI/ML & Data Engineering Specialist (MLOps | Data Engineering |MedTech/Pharma/Manufacturing Analytics) Location: Coimbatore, Chennai, Bangalore and Hyderabad Experience: 58 years Role Type: Full-time/Hybrid Industry: Medical Devices (MedTech) - Pharmaceuticals - Advanced & Smart Manufacturing - Industry 4.0 Keywords: AI/ML Engineering, Data Engineering, Time-Series ML, Deep Learning, MLOps, Cloud/On-Prem Deployment, Medtech & Pharma Industrial Analytics Role Summary We are seeking an experienced AI/ML & Data Engineering Specialist (58 years) to design, build, and deploy end-to-end machine learning pipelines across MedTech, Pharmaceutical, and smart manufacturing environments. This Hybrid Role Combines - Machine Learning (supervised + unsupervised) - Time-series modelling & Deep Learning - Data Engineering (ETL/ELT & feature engineering) - MLOps (CI/CD, containerization, deployment) - Blueprint/Framework creation for scalable ML implementation You will collaborate closely with onsite Business Systems Analysts (BSAs) and domain experts to understand workflows, interpret operational behaviour, and convert them into scalable ML and data workflows. This role requires strong analytical capability, high independence, and comfort working in environments where datasets may be partially structured or evolving. Key Responsibilities - Data Engineering & Feature Development - Build and maintain ingestion pipelines for time-series and operational datasets. - Develop structured data layers and reusable feature engineering components. - Improve data consistency, quality, and ML readiness. - Implement feature stores for training and real-time scoring. - Machine Learning Development Unsupervised ML - Anomaly detection - Behaviour profiling - Drift and stability analysis Supervised ML - Prediction/forecasting models - Regression and classification tasks Deep Learning (Time-Series) - LSTM / GRU models - 1D-CNN - Temporal Convolutional Networks (TCN) - Autoencoder architectures - ML Framework & Blueprint Development - Develop standardized, reusable ML pipelines that scale across multiple machines, sites, or processes. - Build modular ingestion, feature engineering, training, scoring, and monitoring components. - Support replication across MedTech plants, Pharma units, or manufacturing lines. - MLOps & Deployment - Containerize ML pipelines using Docker. - Implement CI/CD workflows (Azure DevOps, GitHub Actions, etc.). - Deploy models in cloud, on-premise, or hybrid environments. - Maintain model monitoring, drift alerts, and retraining automation. - Support API-based and dashboard-based integrations. - Collaboration & Business Alignment - Work closely with onsite BAs and domain SMEs to interpret workflows and operational insights. - Translate business objectives into ML problem definitions and data requirements. - Communicate ML insights clearly to non-technical manufacturing stakeholders. - Document assumptions, workflows, and ML model behavior comprehensively. Required Skills & Experience (58 Years) Technical Skills - Strong Python skills (Pandas, NumPy, SciPy, Scikit-learn) - Experience with Deep Learning (TensorFlow / PyTorch) - Expertise in time-series modelling and feature engineering - ETL/ELT pipeline development and data transformation - Containerization (Docker), Git, CI/CD pipelines - Cloud familiarity (Azure/AWS) and hybrid deployments - API development (FastAPI preferred) Professional Skills - Ability to work independently and handle ambiguous or evolving datasets - Strong analytical and structured problem-solving skills - Excellent communication skills for cross-functional collaboration - Comfortable working in regulated environments with strong documentation expectations Preferred Experience - Experience in MedTech, Pharma, or regulated manufacturing - Exposure to ISO 13485, GxP, CSV, or validation-aligned documentation - Familiarity with MES, QMS, Historian, or industrial data sources - Background in predictive maintenance or operational intelligence solutions Qualifications - Bachelor's degree in Computer Science, Data Engineering, AI/ML, or related fields - Certifications in ML Engineering, MLOps, Cloud, or Industry 4.0 are beneficial