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development and operationalization of Generative AI and Agentic AI systems, including prompt engineering, orchestration frameworks, tool integration, and autonomous agent workflows\nCollaborate with cross-functional teams (Engineering, Data Engineering, Product, QA, DevOps) to deliver robust AI-powered solutions\nProvide technical leadership and mentorship to data scientists and ML engineers, driving capability development and code quality standards\nEnsure AI solutions comply with organizational governance frameworks, including security, privacy, ethical AI, and regulatory requirements\nEvaluate and integrate modern AI/ML tools, frameworks, and platforms (e.g., model serving frameworks, vector databases, orchestration tools)\nTranslate complex business problems into scalable AI solutions and communicate outcomes effectively to stakeholders and leadership\nPerson Specifications\nBachelor's degree in IT, Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or a related field\n4-5 years of professional experience in Data Science, AI, or ML, working in production-grade environments\nTechnical Expertise\nStrong hands-on experience in Machine Learning and Deep Learning (supervised/unsupervised learning, NLP, computer vision) \nPractical experience in Generative AI (LLMs, prompt engineering, RAG pipelines, embeddings, fine-tuning)\nExperience designing and implementing Agentic AI systems (multiagent orchestration, tool usage, autonomous workflows)\nSolid knowledge of MLOps practices (model lifecycle management, CI/CD, monitoring, retraining pipelines)\nExperience in LLMOps (prompt/version management, evaluation frameworks, guardrails, cost optimization, observability)\nProficiency in Python and common AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)\nExperience with cloud platforms (AWS, Azure, or GCP) and cloudnative platforms & services (e.g., Copilot Studio, Bedrock, Vertex AI, Azure OpenAI)\nFamiliarity with data engineering concepts (ETL pipelines, feature stores, data lakes/warehouses)\nLeadership & Soft Skills\nStrong architectural thinking and problem-solving skills with the ability to design scalable AI systems\nProven ability to lead teams, mentor engineers, and drive delivery in fast-paced environments\nExcellent communication skills with the ability to engage both technical and non-technical stakeholders\nExperience working within governance frameworks (e.g., AI governance, security, compliance)\nStrong ownership mindset with a focus on quality, performance, and business impact\nVendor submissions - 12 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