Senior AI/ML Backend Developer – Python

About the Role

Develop and maintain our AI module within the Intelycx manufacturing platform. You’ll build intelligent features for predictive analytics, anomaly detection, and process optimisation while ensuring seamless integration with our Django backend and modular architecture, and to evolve the current platform into a standalone platform capable of ingesting information from third-party platforms.

Must Haves

• Proven Python development with production ML/AI systems
• Machine Learning frameworks: PyTorch or TensorFlow, scikit-learn
• ML Operations: Model training, versioning, deployment, monitoring
• Django/FastAPI: Building ML-enabled REST APIs
• Data engineering: Pandas, NumPy, data preprocessing pipelines
• SQL expertise: Complex queries, optimisation, time-series data
• pgVector, OpenSearch or any other vector-based databases.
• Model optimisation: Memory efficiency, inference speed, batch processing
• Testing ML systems: Unit tests, model validation, A/B testing
• Docker for containerised ML services
• Git/CI/CD for ML workflows

Nice to Haves

• LLM experience: OpenAI API, prompt engineering, RAG, and MCP systems
• MLOps tools: MLflow, Weights & Biases, Kubeflow
• Time-series forecasting for manufacturing data
• Anomaly detection algorithms and statistical methods
• Computer vision for quality inspection
• NLP/text analysis for batch records and documentation
• Celery for async ML task processing
• Redis/caching strategies for model results
• Manufacturing domain knowledge (IoT sensors, production metrics)
• Experience with resource-constrained environments (memory optimisation)

Technical Context

• AI module integrated with a Django-based manufacturing platform
• Real-time processing of IoT sensor data
• Predictive maintenance and quality control use cases
• Must handle dual-database architecture (main + serverless)
• Integration with the existing EBR (Electronic Batch Records) system
• Scalability for multi-factory deployments

Key Responsibilities

• Design and implement ML models for manufacturing insights
• Build efficient data pipelines for training and inference
• Optimise models for production performance and memory usage
• Create APIs for AI feature consumption
• Monitor model performance and implement retraining strategies
• Collaborate with the frontend team on the AI feature UX