Primary Responsibilities Translate business problems into AI/ML, Generative AI, and Agentic AI solution approaches Conduct hands-on experimentation using machine learning, Generative AI, Agentic AI, and emerging AI technologies Design, build, and validate proof-of-concepts (POCs) and prototypes to assess technical feasibility, business value, scalability, and operational readiness Develop production-oriented POCs that establish implementation patterns, reusable assets, architecture guidance, deployment approaches, and operational considerations required for enterprise adoption Create reusable prompts, workflows, evaluation frameworks, reference architectures, solution accelerators, and implementation assets for broader organizational adoption Drive successful transition of validated POCs into production by partnering closely with engineering teams to ensure solutions are scalable, maintainable, secure, and aligned with enterprise architecture standards Develop implementation-ready artifacts including reusable code components, prompt libraries, workflow templates, deployment recommendations, evaluation methodologies, and technical documentation to accelerate engineering adoption Own the technical readiness of AI solutions by proactively identifying scalability constraints, operational dependencies, implementation risks, and mitigation strategies during experimentation Apply AI Development Lifecycle (AIDLC) practices during experimentation phases, including: Structured evaluation and benchmarking Iterative model refinement Experiment tracking and documentation Performance and cost optimization Document learnings, experimentation results, architectural recommendations, and reusable solution assets Develop Generative AI solutions leveraging: Retrieval-Augmented Generation (RAG) architectures Prompt engineering and optimization techniques Vector databases and semantic retrieval frameworks AI evaluation and guardrails Build and evaluate Agentic AI workflows, including: Tool integration and orchestration Multi-step reasoning and planning Multi-agent collaboration patterns Autonomous and semi-autonomous workflows Evaluate emerging AI frameworks, platforms, and technology stacks to identify opportunities for innovation, standardization, and enterprise adoption Support development and adoption of AI accelerators, reusable frameworks, and best practices across teams Optimize early-stage solution cost efficiency through: Token usage awareness and optimization Prompt tuning and response management Model selection based on use-case requirements and cost-performance targets Cost-performance tradeoff analysis Collaborate with business, product, architecture, and engineering teams to clarify requirements and align solutions with measurable business outcomes Communicate experimentation results, trade-offs, recommendations, implementation considerations, and business impact to technical and non-technical stakeholders Accelerate organizational AI adoption by reducing the cycle time from experimentation to production deployment through repeatable patterns and reusable assets Measure success through: Quality and business impact of AI/ML, GenAI, and Agentic AI POCs Production readiness of delivered solutions Percentage of POCs successfully adopted and deployed into production Adoption of reusable accelerators, prompts, workflows, and reference architectures Reduction in experimentation-to-production cycle time Delivery of measurable business outcomes enabled through productionized AI solutions Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities. Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle Comply 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 so Requirements Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Artificial Intelligence, or related field; Master's degree preferred 5+ years of experience delivering AI/ML solutions with strong ownership of enterprise-scale AI initiatives Experience translating business challenges into effective AI/ML solution strategies Experience designing, developing, and delivering successful AI proof-of-concepts that progressed into production environments Hands-on experience with Generative AI technologies, including: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Prompt engineering and evaluation Embeddings and vector database technologies Experience with Agentic AI frameworks, orchestration platforms, and tool integration patterns Experience with data pipelines, feature engineering, experimentation frameworks, and model evaluation methodologies Cloud platform experience across Azure, AWS, and/or Google Cloud Platform Experience optimizing AI systems through model selection, token utilization, and cost-performance tuning Solid programming experience in Python and SQL Solid experience applying AI Development Lifecycle (AIDLC) principles, experimentation methodologies, and benchmarking frameworks Solid expertise in machine learning, deep learning, experimentation, and model development Deep learning expertise using PyTorch and/or TensorFlow Proven ability to collaborate effectively with engineering organizations to enable successful production adoption of AI solutions Proven solid analytical, problem-solving, communication, and stakeholder management skills Proven ability to collaborate effectively across business, product, engineering, and leadership teams Preferred Qualifications Experience building enterprise-scale Generative AI and Agentic AI solutions Experience with vector databases such as Pinecone, FAISS, Weaviate, Chroma, pgvector, or Azure AI Search Experience establishing AI experimentation frameworks, evaluation methodologies, governance models, and production-readiness standards Experience developing reusable accelerators, AI platforms, innovation frameworks, or reference architectures Healthcare domain experience including claims, clinical data, EHR/FHIR, healthcare analytics, care management, or operational workflows Experience mentoring teams and driving AI capability development across organizations Healthcare domain experience: claims, EHR/HL7/FHIR, coding (ICD/CPT), risk adjustment, quality measures, de-identification Knowledge of Responsible AI, model governance, AI risk management, and enterprise AI controls Familiarity with frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, LangGraph, or similar platforms Contributions to patents, technical publications, internal frameworks, accelerators, or enterprise AI innovation initiatives Big data platforms (Databricks, Snowflake, BigQuery) and streaming (Kafka); lakehouse patterns MLOps stack: MLflow/SageMaker/Azure ML/Vertex; model monitoring/observability Vector databases (FAISS, Pinecone, pgvector), knowledge graphs (Neo4j), and ontologies (UMLS/SNOMED) Security/compliance frameworks (SOC 2, HITRUST) Additional languages for performance or integration (Scala/Java/Go)