• AI/ML Model Development Design and implement supervised, unsupervised, and reinforcement learning models for predictive analytics, classification, and optimization tasks across engineering and operational domains.
• Advanced Data Engineering Build scalable data pipelines for ingestion, transformation, and labeling using tools like Airflow, Kafka, and Databricks; ensure data quality, lineage, and consistency.
• Model Deployment & MLOps Deploy and monitor AI/ML models in production using Docker, Kubernetes, and Azure ML; implement continuous learning loops and model explainability frameworks.
• Agentic AI & Intelligent Systems Develop autonomous AI agents with reasoning and decision-making capabilities; integrate LLMs, knowledge graphs, and orchestration frameworks for hybrid AI solutions.
• Cross-Functional Collaboration Partner with domain experts to translate business and engineering challenges into AI-driven solutions; contribute to internal AI communities and reusable asset libraries.
• Governance & Compliance Ensure adherence to data governance, cybersecurity, and ethical AI standards; maintain thorough documentation and reproducibility across all AI initiatives.

You will join a high performing team, where you will be able to thrive.

Qualifications for the role

• Education & Experience - Bachelor’s degree in Computer Science, Electrical, Electronics, Instrumentation, or a related engineering field, with 2–5 years of experience in AI/ML engineering, data science, or applied machine learning.
• Programming & Model Development - Strong proficiency in Python with hands-on experience in developing, fine-tuning, and deploying ML/DL models using frameworks such as TensorFlow, PyTorch, Scikit-learn, or Hugging Face.
• Machine Learning & Generative AI - Solid understanding of ML theory, statistical modeling, feature engineering, and data preprocessing; familiarity with NLP and LLM-based agent frameworks like LangChain, LlamaIndex, AutoGen, or Semantic Kernel.
• API Development & Data Engineering - Experience in building REST APIs, integrating ML models into enterprise applications, and automating pipelines using FastAPI, Flask, or Airflow; strong knowledge of SQL, NoSQL, and Graph databases (e.g., PostgreSQL, MongoDB, Neo4j).
• MLOps & Deployment - Exposure to containerization and deployment tools such as Docker, Kubernetes, and Azure ML; understanding of data versioning, ETL automation, and metadata management using tools like DVC, MLflow, or Databricks.
• Industrial AI & Preferred Skills - Experience in time-series analysis, predictive maintenance, and anomaly detection; awareness of industrial protocols (OPC-UA, Modbus, MQTT); familiarity with cloud AI platforms, vector databases, digital twins, and CI/CD practices; strong analytical and communication skills.