About the Role

In this role, you will have the opportunity to act as a design authority for an engineering discipline with low to medium complexity. Each day, you will complete assignments on small projects or portions of larger projects in a cost-effective manner. You will also showcase your expertise by applying basic design thinking/design for excellence concepts in projects.

The work model for the role is hybrid, and this role contributes to the Process Automation business in Energy Industries based in Bangalore, India.

Primary Accountabilities

• 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.

Qualifications

• Education & Experience: Bachelors 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.