01Responsibilities
AI Strategy & Technical Leadership Drive AI solution strategy for complex and high-impact business problems Lead technical design, solution architecture, and AI technology selection decisionsEstablish reusable AI patterns, frameworks, standards, and best practicesProvide technical leadership and mentorship to Applied Data Scientists and cross-functional teamsEvaluate emerging AI technologies and recommend enterprise adoption approachesApplied AI Solution Development Translate business challenges into scalable AI, ML, GenAI, and Agentic AI solutionsDesign and develop POCs, prototypes, and reference implementationsExtensive experience in areas by providing solutions using machine learning, deep learning, NLP, recommendation systems, forecasting, multimodal AI, and Generative AI techniquesBuild reusable assets including prompts, workflows, evaluation frameworks, and implementation acceleratorsDefine production-ready solution blueprints to support engineering adoptionGenerative AI & Agentic AI Design and implement solutions using LLMs, RAG, semantic search, vector retrieval, and prompt engineering techniquesDevelop agentic workflows leveraging orchestration frameworks, tool integration, planning, reasoning, and multi-agent collaborationEstablish evaluation, guardrail, and governance frameworks for GenAI applicationsOptimize solution performance, quality, and cost efficiencyProduction Readiness & MLOps Drive successful transition of validated AI solutions into production environmentsApply AI Development Lifecycle (AIDLC) and MLOps best practices across experimentation and deploymentEnsure scalability, observability, monitoring, model governance, and operational readinessCollaborate with engineering and platform teams to operationalize AI solutionsTeam & Organizational Impact Lead a small team of Applied Data Scientists while remaining hands-onMentor team members on AI methodologies, experimentation practices, and technical excellencePromote knowledge sharing, innovation, and adoption of reusable AI capabilitiesInfluence enterprise AI strategy, architecture standards, and capability developmentComply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do soRequired Qualifications:
Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Artificial Intelligence, or related field; Master's preferred15+ years of experience delivering enterprise AI/ML solutionsHands-on experience with Generative AI technologies including LLMs, RAG, prompt engineering, embeddings, vector databases, agentic systemsExperience developing Agentic AI solutions using orchestration frameworks and tool-enabled workflowsExperience with Azure ML, SageMaker, Vertex AI, MLflow, Docker, Kubernetes, and cloud-native AI platformsExperience mentoring data scientists and leading small technical teamsProven experience leading complex AI initiatives from ideation through production deploymentExpertise in Python, SQL, PyTorch, and/or TensorFlowSolid expertise in machine learning, deep .
