Job Description
Job Summary We are seeking an experienced AI / Machine Learning Engineer to drive data-driven transformation across our manufacturing operations. In this role, you will collaborate closely with manufacturing, operations, and business teams to identify high-impact AI/ML use cases and deliver end-to-end machine learning solutions. You will work with large-scale manufacturing data to improve quality, efficiency, and operational decision-making. Key Roles & Responsibilities Identify & Groom AI/ML Use Cases Partner with manufacturing, operations, and business stakeholders to identify opportunities where AI and machine learning can solve real-world problems, improve efficiency, and create measurable value. Define project scope, objectives, and success metrics. End-to-End Machine Learning Delivery Own machine learning initiatives from ideation through production. This includes data collection, data cleaning, feature engineering, model development, validation, deployment, and ongoing monitoring in production environments. Programming & Data Engineering Develop clean, scalable, and efficient code primarily using Python. Build data pipelines, automate analytical workflows, and support ML model training and deployment. Use SQL extensively for data extraction, transformation, and analysis. Data Analysis & Modeling Analyze large and complex manufacturing datasets from systems such as MES, ERP, and IoT sensors. Identify patterns, engineer meaningful features, and develop predictive or prescriptive models for use cases such as quality improvement, predictive maintenance, and process optimization. Insight Communication & Stakeholder Engagement Translate complex technical analyses into clear, actionable insights and recommendations for non-technical stakeholders, enabling data-driven decision-making across the organization. Candidate Expectations & Qualifications Experience 3+ years of hands-on experience in AI, Machine Learning, or Data Science. Prior experience in manufacturing, industrial, or operations-focused environments is strongly preferred. Technical Skills Strong proficiency in Python and ML libraries such as scikit-learn, TensorFlow, and PyTorch. Solid SQL skills for data querying, transformation, and analysis. Experience with data visualization tools such as Power BI or Tableau. Basic understanding of MLOps concepts, including model deployment, monitoring, and lifecycle management. Domain Knowledge (Preferred) Understanding of manufacturing processes and data sources such as MES, ERP, and IoT systems. Familiarity with common manufacturing challenges, including quality control, predictive maintenance, process optimization, and order backlog management. Soft Skills Strong analytical and problem-solving mindset with the ability to decompose complex problems into data-driven solutions. Excellent communication and collaboration skills, enabling effective interaction with cross-functional teams ranging from plant operators to senior leadership. Nice-to-Have Skills Experience working with cloud platforms such as AWS, Azure, or Google Cloud Platform. Knowledge of time-series analysis, anomaly detection, and optimization techniques.