Associate AI/ML Engineer

About the Role:

As an Associate AI/ML Engineer reporting to the Technology Manager, you will act as a design authority for engineering disciplines of low to medium complexity. You will contribute to the Process Automation business within the Energy Industries, based in Bangalore, India. Your daily tasks will involve completing assignments on small projects or portions of larger projects cost-effectively, applying basic design thinking and design for excellence concepts.

Key Responsibilities:

• AI/ML Model Development: Design and implement supervised, unsupervised, and reinforcement learning models for predictive analytics, classification, and optimization across engineering and operational domains.
• Advanced Data Engineering: Build scalable data pipelines for ingestion, transformation, and labeling using tools such as Airflow, Kafka, and Databricks, ensuring data quality, lineage, and consistency.
• Model Deployment & MLOps: Deploy and monitor AI/ML models in production environments 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 Large Language Models (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 be part of a high-performing team where you can thrive and develop your career.

Qualifications:

• 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 like 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 Natural Language Processing (NLP) and LLM-based agent frameworks such as LangChain, LlamaIndex, AutoGen, or Semantic Kernel.
• API Development & Data Engineering: Experience 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 like Docker, Kubernetes, and Azure ML. Understanding of data versioning, ETL automation, and metadata management using tools such as 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 are essential.