Overview

Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

Responsibilities

• GenAI Innovation &
• Technology Acceleration
• Lead the exploration, evaluation, and application of emerging GenAI technologies, with a strong focus on Agentic AI, conversational agents, autonomous workflows, multi-agent systems, and LLM-enabled enterprise automation
• Develop rapid prototypes, proof-of-concepts, technical accelerators, and reusable solution patterns that help accelerate AI adoption across business and technology teams
• Assess new AI frameworks, orchestration patterns, model capabilities, evaluation techniques, and deployment approaches to determine enterprise applicability, scalability, and risk
• Translate innovation concepts into practical engineering blueprints, reference implementations, and production-ready solutions
• Agentic AI Solution Design & • Implementation
• Design and implement Agentic AI solutions using Python and modern AI orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, ReACT, ReWOO, RAG, and agent-to-agent communication patterns
• Build intelligent agents capable of reasoning, planning, tool usage, memory management, API interaction, multi-step workflow execution, and context-aware conversational experiences
• Develop conversational AI systems that support multi-turn interactions, enterprise knowledge retrieval, user intent handling, task completion, personalization, and seamless integration with backend systems
• Implement RAG-based architectures using vector databases, embeddings, document processing pipelines, semantic search, reranking, grounding, and context optimization techniques
• Technical Engineering & • Hands-On Delivery
• Contribute directly to software design, application development, prompt engineering, agent orchestration, evaluation pipelines, and production implementation
• Convert architectural guidance and innovation ideas into scalable, maintainable, secure, and well-tested engineering deliverables
• Develop APIs, microservices, reusable libraries, and platform components that enable GenAI capabilities to be integrated into enterprise applications
• Apply strong software engineering practices to ensure AI solutions are reliable, modular, extensible, observable, and production ready
• Cloud-Native AI Development
• Build and deploy secure, scalable, cloud-native AI solutions on Azure, using services such as Azure OpenAI, Azure AI Search, Azure Functions, Azure Kubernetes Service, Cosmos DB, Azure Container Apps, API Management, Key Vault, and related platform services
• Design solutions using microservices, event-driven architectures, serverless patterns, containerized workloads, and scalable cloud infrastructure
• Ensure AI solutions comply with enterprise standards for security, reliability, privacy, observability, resiliency, and operational excellence
• LLMOps, Evaluation & • AI Reliability
• Develop evaluation frameworks for LLM and agentic systems, including response quality, factuality, hallucination risk, grounding accuracy, task completion rate, latency, cost, safety, and user experience
• Implement guardrails, safety checks, content filtering, fallback strategies, prompt versioning, model monitoring, and human-in-the-loop review mechanisms where appropriate
• Create automated testing and benchmarking approaches for prompts, agents, tools, workflows, RAG pipelines, and conversational experiences
• Continuously improve agent performance through experimentation, prompt refinement, retrieval optimization, model selection, tool design, and feedback loops
• Performance Optimization
• Analyze and improve the performance of AI systems, including response latency, throughput, token usage, retrieval accuracy, context window efficiency, cost optimization, and multi-turn conversation quality
• Profile and optimize AI workflows across model calls, retrieval pipelines, API integrations, orchestration layers, and backend services
• Identify engineering bottlenecks and implement improvements that enhance scalability, reliability, and user experience
• DevOps, Deployment & • Operational Readiness

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, or a related technical field; equivalent practical experience will also be considered
• 8+ years of experience in software engineering, AI/ML engineering, platform engineering, or large-scale distributed systems
• Hands-on experience with AI agent frameworks and LLM orchestration tools such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar frameworks
• Solid hands-on experience building AI/ML, GenAI, or LLM-powered applications in production or enterprise environments
• Solid understanding of Agentic AI architectures, including planning, reasoning, tool usage, memory, function calling, agent orchestration, multi-agent collaboration, and workflow automation
• Deep understanding of LLM-driven architectures, prompt engineering, prompt optimization, model evaluation, context management, and reliability guardrails
• Solid knowledge of DevOps, MLOps, or LLMOps practices, including automated testing, deployment pipelines, monitoring, logging, and operational support
• Solid testing discipline, including unit testing, integration testing, regression testing, prompt testing, and evaluation automation using tools such as pytest or equivalent frameworks
• Proficiency in Python and strong understanding of software engineering fundamentals, including data structures, APIs, microservices, testing, observability, and scalable system design
• Proven ability to benchmark and optimize AI applications for latency, throughput, accuracy, cost, scalability, and user experience
• Proven ability to lead engineering teams, mentor developers, review code, guide technical decisions, and deliver high-quality solutions
• Proven solid communication, collaboration, and stakeholder management skills, with the ability to translate business needs into technical solutions and explain complex AI concepts clearly
• Exposure to speech, voice, NLU, NLP, or multimodal AI capabilities
• Understanding of responsible AI principles, including fairness, transparency, privacy, security, safety, explainability, and human oversight