Lead Applied AI Engineer

🕒 February 26

🏢🏡 New York City – Hybrid

💵 $170.8k - $234.8k / year

⏰ Full Time

🟠 Senior

🤖 AI Engineer

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Humana

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1961

⚕️ Healthcare Insurance

Healthcare Insurance

Humana is a healthcare company dedicated to making a positive impact on the health of individuals, communities, and the healthcare system as a whole. With a focus on putting health first, Humana serves a diverse range of populations, including seniors and the military, providing Medicare Advantage HMO, PPO, and PFFS plans. Humana is committed to fostering a culture of belonging and mutual respect, offering competitive and flexible benefits to ensure the financial security of its employees and their families. The company prides itself on creating an inclusive workplace where everyone has the opportunity to succeed.

📋 Description

• Architect comprehensive end-to-end AI systems including sophisticated RAG pipelines with multi-stage retrieval and re-ranking, complex agent orchestration systems that coordinate multiple specialized agents, and multi-model integrations that leverage different AI models for their respective strengths, designing these systems with appropriate modularity, extensibility, and operational characteristics to support evolving business requirements. • Define rigorous standards for prompt engineering including templates, versioning, and testing methodologies, establish comprehensive evaluation metrics that capture both technical performance and business value, and develop performance optimization strategies including model selection criteria, caching approaches, and resource utilization patterns that teams across the organization can adopt to accelerate AI delivery. • Lead deployment of AI systems into production environments with strong observability including detailed logging and tracing, comprehensive reliability including graceful degradation and circuit breakers, thorough monitoring including real-time dashboards and automated alerting, and robust incident response procedures, ensuring AI services meet stringent service level objectives required for healthcare applications. • Design scalable data ingestion architectures that can process diverse data sources including structured databases, unstructured documents, and real-time streams, implement efficient retrieval architectures using vector databases and hybrid search approaches, develop data preprocessing pipelines that clean and enrich data for AI consumption, and establish data quality monitoring to ensure AI systems operate on high-quality inputs. • Drive quantitative evaluation and continuous improvement of AI systems through establishment of evaluation frameworks, implementation of A/B testing capabilities, analysis of user feedback and system telemetry, and systematic iteration on prompts, retrieval strategies, and model configurations to progressively improve system performance and user satisfaction over time. • Collaborate strategically with platform teams to ensure infrastructure readiness for demanding AI workloads including GPU availability, appropriate networking configurations, and optimized data storage, define requirements for AI-specific platform capabilities such as model serving infrastructure and feature stores, and partner on integration of AI systems with enterprise services. • Mentor engineers at various levels through technical guidance, code reviews, architecture discussions, and career development support, elevate AI engineering best practices across the organization through creation of documentation, delivery of training sessions, and establishment of communities of practice, and foster a culture of responsible AI development that prioritizes ethics, transparency, and user benefit. • Ensure AI solutions rigorously meet healthcare compliance requirements through comprehensive documentation of system behavior and decision logic, implementation of ethical standards that prevent algorithmic bias and ensure fairness across different populations, and adherence to regulatory frameworks including HIPAA, FDA guidance for clinical decision support, and emerging AI-specific regulations.

🎯 Requirements

• 7+ years of experience in software engineering with a strong focus on applied AI/ML, building and operating distributed systems at scale, and developing full-stack architectures that combine backend services with modern web applications, demonstrated through leadership of significant projects that delivered measurable business impact through AI capabilities. • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field, or equivalent practical experience demonstrated through significant technical leadership in AI projects, recognized contributions to the AI engineering community, or progressive career advancement into increasingly responsible AI technical leadership roles. • Demonstrated deep expertise designing and deploying production-grade generative AI systems including sophisticated RAG architectures with multi-hop retrieval and reasoning, agent orchestration frameworks that coordinate multiple AI agents with tool use and memory, multi-model systems that combine different AI capabilities, and conversational AI systems that maintain context and handle complex dialogues. • Proven ability to lead complex AI initiatives across multiple teams with different specializations, translating high-level business objectives into concrete AI system designs and technical roadmaps, coordinating implementation across frontend, backend, data, and infrastructure teams, and driving projects from conception through production deployment and ongoing optimization. • Strong technical proficiency in Python including advanced language features and design patterns, extensive experience with modern web application frameworks (e.g., React, FastAPI) including best practices for scalability and maintainability, and deep knowledge of AI-specific technologies including vector databases, embedding models, LLM APIs, and orchestration frameworks. • Demonstrated experience establishing organization-wide best practices for prompt engineering including systematic testing and version control, comprehensive evaluation frameworks that combine automated metrics with human assessment, model observability including tracking of costs and performance, and performance benchmarking methodologies that enable data-driven optimization decisions. • Deep familiarity with responsible AI principles including fairness, accountability, transparency, and ethics, understanding of governance considerations for AI systems including model risk management and validation requirements, and practical experience addressing deployment challenges in regulated environments including testing, documentation, change management, and ongoing monitoring requirements.

🏖️ Benefits

• medical, dental and vision benefits • 401(k) retirement savings plan • time off (including paid time off, company and personal holidays, volunteer time off, paid parental and caregiver leave) • short-term and long-term disability • life insurance • and many other opportunities.