Associate Lead - Data Science

Key Responsibilities Lead the development and deployment of advanced data science and AI solutions across Machine Learning (ML), Deep Learning (DL), Generative AI, and Agentic AI use cases Lead the end-to-end model lifecycle management, including data ingestion, feature engineering, model development, evaluation, deployment, monitoring, and retraining Lead the development and operationalization of Generative AI and Agentic AI systems, including prompt engineering, orchestration frameworks, tool integration, and autonomous agent workflows Collaborate with cross-functional teams (Engineering, Data Engineering, Product, QA, DevOps) to deliver robust AI-powered solutions Provide technical leadership and mentorship to data scientists and ML engineers, driving capability development and code quality standards Ensure AI solutions comply with organizational governance frameworks, including security, privacy, ethical AI, and regulatory requirements Evaluate and integrate modern AI/ML tools, frameworks, and platforms (e.g., model serving frameworks, vector databases, orchestration tools) Translate complex business problems into scalable AI solutions and communicate outcomes effectively to stakeholders and leadership Person Specifications Bachelor's degree in IT, Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or a related field 4-5 years of professional experience in Data Science, AI, or ML, working in production-grade environments Technical Expertise Strong hands-on experience in Machine Learning and Deep Learning (supervised/unsupervised learning, NLP, computer vision) Practical experience in Generative AI (LLMs, prompt engineering, RAG pipelines, embeddings, fine-tuning) Experience designing and implementing Agentic AI systems (multiagent orchestration, tool usage, autonomous workflows) Solid knowledge of MLOps practices (model lifecycle management, CI/CD, monitoring, retraining pipelines) Experience in LLMOps (prompt/version management, evaluation frameworks, guardrails, cost optimization, observability) Proficiency in Python and common AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) Experience with cloud platforms (AWS, Azure, or GCP) and cloudnative platforms & services (e.g., Copilot Studio, Bedrock, Vertex AI, Azure OpenAI) Familiarity with data engineering concepts (ETL pipelines, feature stores, data lakes/warehouses) Leadership & Soft Skills Strong architectural thinking and problem-solving skills with the ability to design scalable AI systems Proven ability to lead teams, mentor engineers, and drive delivery in fast-paced environments Excellent communication skills with the ability to engage both technical and non-technical stakeholders Experience working within governance frameworks (e.g., AI governance, security, compliance) Strong ownership mindset with a focus on quality, performance, and business impact Vendor submissions - 12 months