Role Overview :

We are looking for an ML Engineer with strong experience in Machine Learning, LLMs/Generative AI and MLOps to design, develop, deploy and maintain production-grade AI/ML solutions.

The role involves working with real-world industrial and IoT data and building intelligent solutions that can transform large volumes of sensor, equipment, energy and operational data into meaningful insights.

You will work closely with Data Science, Engineering and Product teams to take ML/GenAI solutions from experimentation to production.

Key Responsibilities :

- Design, develop and productionise Machine Learning models for real-world business and industrial use cases.

- Build and deploy LLM/GenAI-based applications for intelligent insights, knowledge retrieval, summarisation and recommendation systems.

- Develop solutions using RAG, embeddings, vector databases, prompt engineering and LLM APIs.

- Build ML pipelines covering data preparation, feature engineering, model training, evaluation and deployment.

- Implement and maintain MLOps workflows for model versioning, deployment, monitoring and continuous improvement.

- Develop scalable AI services and APIs using Python and relevant ML frameworks.

- Work with large-scale time-series, sensor, energy and equipment data.

- Build solutions for anomaly detection, predictive maintenance, forecasting and asset performance optimisation.

- Monitor model performance, data drift and model/data quality in production.

- Collaborate with engineering teams to integrate ML models into production applications and platforms.

- Ensure AI/ML solutions are scalable, reliable, observable and production-ready.

- Evaluate emerging GenAI/LLM technologies and identify opportunities to apply them to business and industrial use cases.

Required Skills :

- 4 to 7 years of experience in Machine Learning / AI Engineering.

- Strong programming skills in Python.

- Strong understanding of Machine Learning concepts, algorithms and model development.

- Hands-on experience with LLMs / Generative AI.

- Experience with RAG, embeddings, vector databases, prompt engineering and LLM APIs.

- Experience with frameworks such as PyTorch, TensorFlow or scikit-learn.

- Good understanding of MLOps and ML model lifecycle management.

- Experience with Docker and cloud platforms such as AWS / Azure / GCP.

- Experience building REST APIs using FastAPI / Flask is preferred.

- Understanding of CI/CD, model deployment, monitoring and observability.

- Experience working with structured and unstructured data.

Value-Added Skills :

Candidates with exposure to any of the following will have an added advantage :

- Industrial IoT (IIoT) / IoT

- Sensor data and time-series analytics

- Energy management / energy analytics

- Manufacturing / industrial automation

- Smart buildings / Building Management Systems (BMS)

- Equipment monitoring and predictive maintenance