AI/ML Engineer

Client: TCS | Engagement: Full-time | Work mode: HYBRID | Rate: 75-78 USD | Experience: 6+ | Publisher job id: 10856066

Role Overview

We are seeking an AI/ML Engineer with hands-on experience building, fine-tuning, and deploying LLM-based solutions. This role involves working on Natural Language Processing (NLP) and Generative AI (GenAI) use cases such as classification, summarization, and retrieval-augmented generation (RAG), in partnership with product and engineering teams to deliver scalable, secure, and measurable outcomes. Key Responsibilities: Design, build, and fine-tune NLP/LLM solutions for business use cases (e.g., classification, summarization, Q&A). Develop efficient, well-documented Python code for training, inference, and evaluation pipelines. Build RAG applications using embeddings, vector databases, and prompt engineering techniques. Integrate LLM applications into services/APIs and ensure performance, reliability, and scalability. Establish model evaluation, monitoring, and governance practices (quality, safety, bias, drift). Collaborate with data engineering and platform teams on data pipelines, deployments, and CI/CD. Required Skills: 6+ years of overall experience in software development focusing on AI/ML engineering. 2+ years of hands-on experience with deep learning for NLP/GenAI. Strong Python proficiency, including writing production-quality, testable, maintainable code. Experience with deep learning frameworks and libraries: PyTorch or TensorFlow; Hugging Face Transformers. Solid understanding of deep learning architectures and modern NLP/LLM concepts (tokenization, attention/transformers, fine-tuning approaches). Experience building rapid prototypes and APIs using FastAPI/Flask and/or Streamlit. Qualifications: 6+ years of overall experience in software development focusing on AI/ML engineering. 2+ years of hands-on experience with deep learning for NLP/GenAI. Strong Python proficiency. Experience with PyTorch or TensorFlow; Hugging Face Transformers. Solid understanding of deep learning architectures and modern NLP/LLM concepts. Experience building rapid prototypes and APIs using FastAPI/Flask and/or Streamlit. Preferred Skills: Experience with LLM orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or similar). Experience with vector databases and embedding workflows (e.g., FAISS, Pinecone, Weaviate, Chroma, Azure AI Search). Experience deploying and scaling ML/LLM workloads on cloud platforms (Azure preferred; GCP/AWS acceptable). Familiarity with agentic architectures and multi-agent patterns (e.g., AutoGen or similar). Healthcare domain knowledge and/or experience building solutions in regulated environments. Standard Technical Skills: MLOps & Deployment: Model packaging and serving, CI/CD, containers (Docker), orchestration (Kubernetes), experiment tracking (MLflow), model registry, monitoring/observability. LLM Evaluation: Offline/online evaluation, prompt/version management, automated testing, hallucination and factuality checks, retrieval evaluation, human-in-the-loop review. Software Engineering: Git, code reviews, unit/integration testing (pytest), REST APIs, basic system design, performance optimization. Security & Compliance: Secure coding, secrets management, PII/PHI handling, access control; familiarity with responsible AI principles is a plus.