AI Engineer - Architect - Tata Consultancy Services (TCS)

Key Responsibilities

• Design and implement end‑to‑end AI architectures, focusing on LLMs, generative AI, and advanced NLP models.
• Develop scalable data processing pipelines with Spark, Hadoop, and Pandas to feed training and inference workloads.
• Deploy models on cloud AI platforms (Azure AI, AWS SageMaker, GCP AI) using Docker and Kubernetes for production‑grade reliability.
• Integrate ML lifecycle tools such as MLflow, Kubeflow, and Airflow to automate training, versioning, and monitoring.
• Build and expose model inference services via REST APIs and microservice frameworks.

Requirements

• Strong proficiency in Python; experience with Java or Scala is a plus.
• Hands‑on expertise with TensorFlow, PyTorch, and Scikit‑learn for deep learning and reinforcement learning tasks.
• Practical knowledge of large language models, prompt engineering, and generative AI techniques.
• Experience deploying AI solutions on Azure AI (or equivalent AWS/GCP services) and managing containerized workloads with Docker and Kubernetes.
• Familiarity with ML workflow tools (MLflow, Kubeflow, Airflow) and building RESTful microservices.