Data Scientist

• Design, develop, and deploy end-to-end AI/ML solutions - with Production support experience (Production Deployment experience)

• Build scalable and robust AI architecture, including LLM-powered

• systems and multi-agent frameworks

• Translate business requirements into actionable AI models and system designs

• Ensure performance, reliability, scalability, and cost optimization of AI solutions

• Develop and implement Generative AI solutions using Large Language Models (LLMs)

• Build Agentic AI systems, including multi-agent orchestration and autonomous workflows

• Design and implement LLM-based assistants, copilots, and conversational AI systems

• Integrate AI solutions with enterprise applications and APIs

• Hands on experience in Natural Language Processing (NLP) , Computer Vision \& Voice AI

• Build and manage ML pipelines (training, validation, deployment, monitoring)

• Ensure model explainability, fairness, and governance compliance

• Implement AI guardrails including safety, bias detection, and hallucination controls

• Ensure adherence to Responsible AI principles (security, privacy, compliance)

• Identify risks /issues and come up with solution/mitigation plans

• Collaboration with internal and stakeholders to ensure delivery as per plan with quality

• Grooming the internal Team , as senior Lead , for technical competence

• Production Experience-To ensure code development \& deployment in production

Core Technical SkillsStrong experience in:

• Machine Learning \& Deep Learning frameworks (TensorFlow, PyTorch, etc.)

• Generative AI \& LLM ecosystems (OpenAI, Hugging Face, LangChain, etc.)

• Agentic AI / Multi-agent system design

Expertise in:

• NLP, Computer Vision, and Conversational AI

• Model deployment (MLOps, APIs, cloud platforms)

• Data processing and feature engineering

• Expertise in source safing tools like Github/GitLab/Bitbucke

• Langgraph /Langchain use

• Chatbot concepts

• Text mining

• Hands-on experience with Cloud platforms (AWS / Azure / GCP)

• Experience in ML Ops tools, CI/CD pipelines, and containerization (Docker/Kubernetes)

• Strong programming skills in Python and relevant AI/ML libraries