About the Role

As an Associate AI/ML Engineer, you will serve as a design authority for engineering disciplines of low to medium complexity. You will be responsible for completing assignments on small projects or segments of larger projects efficiently. Utilizing basic design thinking and design for excellence concepts, you will contribute to innovative solutions.

Join a high-performing team where you can thrive and grow.

Key Responsibilities

• 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

  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.