About the role
- Design, develop, and deploy machine learning solutions that solve complex manufacturing and industrial business problems.
- Build predictive and prescriptive machine learning models for manufacturing operations and production optimization.
- Develop AI models for:
- Predictive Maintenance
- Predictive Quality
- Anomaly Detection
- Root Cause Analysis
- Throughput Prediction
- Cycle Time Prediction
- Capacity Prediction
- Production Scheduling Optimization
- Operator Assistance Recommendations
- Develop intelligent Digital Twin models across:
- Station Twin
- Product Twin (SFC)
- Line Twin
- Warehouse Twin
- Build AI capabilities supporting:
- Process Deviation Detection
- Robot Behavior Prediction
- Conveyor Congestion Prediction
- Torque Anomaly Detection
- Vision Inspection Optimization
- Remaining Process Time Estimation
- Yield Prediction
- Simulation Calibration
- Design and develop feature engineering pipelines, scalable training datasets, and streaming data transformation workflows.
- Build and maintain real-time machine learning pipelines using operational technology (OT) telemetry, IIoT devices, and streaming platforms.
- Collaborate with Product Architects, Manufacturing SMEs, Software Engineers, and Data Engineers to deliver enterprise-grade AI solutions.
- Build and maintain MLOps capabilities including:
- Model Training Pipelines
- Model Deployment Pipelines
- Model Registry
- Feature Store
- Drift Monitoring
- Experiment Tracking
- Automated Retraining Workflows
- CI/CD for Machine Learning
- Work with structured, unstructured, time-series, and streaming datasets to develop scalable AI solutions.
- Ensure production-ready deployment, monitoring, scalability, and continuous improvement of AI models.
- Support digital transformation initiatives across manufacturing plants through intelligent automation and advanced analytics.
- Participate in plant visits to understand manufacturing operations, industrial workflows, and operational challenges.
What We Seek In You
- 3 to 5 years of experience in Machine Learning Engineering, Applied AI, Data Science, or AI Engineering.
- Strong programming expertise in:
- Python
- SQL
- Exposure to Go programming is an added advantage.
- Strong hands-on experience developing machine learning models using:
- Scikit-learn
- XGBoost
- LightGBM
- PyTorch or TensorFlow
- Strong understanding of:
- Time Series Forecasting
- Classification
- Regression
- Clustering
- Anomaly Detection
- Feature Engineering
- Model Evaluation
- Hyperparameter Tuning
- Experience building production-grade MLOps solutions using one or more of:
- MLflow
- Azure Machine Learning
- Kubeflow
- SageMaker
- Databricks ML
- Vertex AI
- Strong understanding of:
- Model Registry
- Experiment Tracking
- Model Deployment
- Drift Monitoring
- Feature Store
- Experience working with enterprise data platforms including:
- PostgreSQL
- Kafka
- Azure Data Lake Storage (ADLS)
- Azure Blob Storage
- Delta Lake
- Spark (Preferred)
- TimescaleDB
- Exposure to Microsoft Azure cloud services including:
- Azure Machine Learning
- Azure Kubernetes Service (AKS)
- Azure Data Lake
- Experience working with real-time streaming data and industrial telemetry pipelines.
- Exposure to visualization frameworks such as:
- Plotly
- Streamlit
- Dash
- Grafana
- Familiarity with modern AI frameworks including:
- LangChain
- LangGraph
- Ray
- Vector Databases
- Large Language Model (LLM) APIs
- Strong understanding of Digital Twin concepts, Industrial AI, and intelligent manufacturing systems.
- Experience working in one or more of the following domains:
- Manufacturing
- Industrial Automation
- Smart Factory
- Industrial IoT (IIoT)
- Robotics
- Supply Chain Analytics
- Quality Engineering
- Exposure to:
- OPC-UA
- MQTT
- PLC Signals
- Digital Twin Platforms
- Time Series Analytics
- Simulation Models
- Industry 4.0 / Industry 5.0
- Computer Vision for Manufacturing
- Strong analytical thinking, problem-solving, and debugging capabilities.
- Ability to work with large-scale real-time streaming datasets and production AI systems.
- Excellent communication, stakeholder management, and documentation skills.
- Strong ownership mindset with the ability to deliver production-ready AI solutions.
- Willingness to travel to manufacturing plants for business understanding and solution validation.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Electronics, or a related Engineering discipline.
- Clear objectives to ensure alignment with our mission, fostering your meaningful contribution.
- Abundant opportunities for engagement with customers, product managers, and leadership.
- You'll be guided by progressive paths while receiving insightful guidance from managers through ongoing feedforward sessions.
- Cultivate and leverage robust connections within diverse communities of interest. Choose your mentor to navigate your current endeavors and steer your future trajectory.
- Embrace continuous learning and upskilling opportunities through Nexversity.
- Enjoy the flexibility to explore various functions, develop new skills, and adapt to emerging technologies. Embrace a hybrid work model promoting work-life balance.
- Access comprehensive family health insurance coverage, prioritizing the well-being of your loved ones.
- Embark on accelerated career paths to actualize your professional aspirations.