AI Engineers design, develop, and deploy intelligent software solutions. They bridge the gap between data science and software engineering by transforming machine learning models into scalable APIs, building retrieval-augmented generation (RAG) pipelines, and integrating generative AI or deep learning applications into production environments.

Core Responsibilities
- Model Development & Integration: Select, train, fine-tune, and evaluate advanced machine learning and deep learning models (e.g., Transformers, LLMs) to address specific business challenges.
- Data Pipeline Management: Collect, clean, and preprocess large datasets, ensuring the information fed into models is high-quality, scalable, and secure.
- Deployment & MLOps: Package AI models into microservices or cloud-based applications. Utilize containerization (Docker, Kubernetes)



and CI/CD pipelines to ensure systems are scalable and performant.
- System Monitoring & Retraining: Establish monitoring tools to track model performance and accuracy, automating pipelines for retraining models as current data becomes available.
- Prompt Engineering & Workflow Design: Craft system prompts and design workflows to seamlessly embed AI capabilities (like chatbots, smart categorization, or automation) into existing software.
- Cross-Functional Collaboration: Work closely with product managers, data scientists, and software teams to align AI system capabilities with business goals and user needs.

📌 AI Engineer (Kolkata)
🏢 Tata Consultancy Services
📍 Kolkata