Job Responsibilities

- Works with less structured and more complex AI/ML and data engineering problems, and serves as a technical resource to others across teams.
- Ability to interact with business users, data partners, architects, and product owners to define analytical and AI/ML solution requirements.
- Capable of leading a team of motivated engineers/data scientists and mentoring them technically and functionally in AI/ML, GenAI, and MLOps practices.
- Participate in and lead critical AI/ML technical discussions, solution architecture reviews, and modernization strategy sessions.
- Prepare detailed solution designs involving model architecture, data pipelines, evaluation strategy, and deployment patterns by collaborating with PO, business SMEs, and engineering leaders.
- Translate business requirements into scalable ML system designs and detailed technical documentation.
- Demonstrated ability to prioritize multiple AI/ML workloads and deliver within aggressive project timelines in a fast-paced environment.
- Participate in design reviews, model validation reviews, code reviews, and production-readiness reviews for AI/ML releases.
- Strong knowledge of AI solution lifecycle: data ingestion, feature engineering, model creation, evaluation, deployment, monitoring, and retraining.
- Document complex functional/technical designs, data flows, ML pipelines, and model runbooks to support business and operational needs.
- Ensure timely delivery and quality of all assigned ML/GenAI deliverables.

Must Have –





- Strong hands-on expertise in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, and model deployment tools.
- Experience in developing ML models, deep learning models, LLM finetuning, and Generative AI applications (RAG, embeddings, prompt engineering).
- Experience with MLOps tools such as MLflow, Kubeflow, Airflow, DVC, GitHub Actions, or Azure ML pipelines.
- Strong data engineering and data analysis skills using Pandas, Spark, SQL.
- Expertise in RESTful AI services, microservice-based ML deployment, Docker, Kubernetes.
- Cloud experience: Azure (preferred), AWS or GCP for model training, storage, and deployment.
- Knowledge of vector databases such as FAISS, Pinecone, Milvus, Chroma.
- Strong analytical and problemsolving skills; ability to explain complex AI concepts to both technical and nontechnical audiences.
- Experience working in distributed microservices environments with scalable ML architectures.
- Excellent communication, presentation, and collaboration skills.
- Proven ability in designing ML systems with reliability, scalability, and observability in mind.

Nice to Have –

- Knowledge of Graph databases, Graph ML, MongoDB.
- Experience building agents, copilots, or enterprise GenAI applications.
- Exposure to legacy modernization including migration of analytical workloads to cloud or contemporary ML frameworks.
- Knowledge of NLP pipelines, ASR, image processing, or multimodal AI.
- Experience with R, Spark MLlib, or ONNX model optimization.

📌 Ai Ml Engineer (Telangana)
🏢 Tata Consultancy Services
📍 Telangana