Senior AI Engineer

The Role

We are seeking a Senior AI Engineer to join our AI & Data Group on the AI Orchestration team. This role is responsible for designing, building, operating and scaling the orchestration layer that powers League’s AI-driven healthcare experiences.

Sitting at the intersection of AI systems design, ML productionization, and distributed software engineering, you will take the probabilistic capabilities of large language models and engineer them into reliable, measurable, and scalable product experiences.

This is a hands-on senior engineering role for a practitioner who moves beyond prototypes to own the hard problem of making AI work at scale. You will translate leading edge AI innovation into reliable, secure, and compliant systems. Your work will directly impact how AI is operationalized across League’s digital health ecosystem—ensuring systems are scalable, observable, performant, and safe in a regulated environment.

Key Responsibilities:

AI Orchestration & Systems Design

• Design and build production-grade AI systems, including RAG pipelines, multi-step agents, and LLM-powered features.
• Make principled architecture choices regarding RAG vs. fine-tuning and agentic loops vs. simpler call-and-response patterns.
• Architect for long-term maintainability, ensuring systems fail gracefully and handle non-deterministic outputs predictably.

Evaluation & Quality Assurance

• Build comprehensive evaluation and observability frameworks to measure model accuracy, grounding, and quality drift.
• Implement automated test suites and "LLM-as-judge" pipelines to catch defects before they reach production.
• Set quality standards for AI components and drive improvements based on human feedback loops.

Production Engineering & MLOps

• Create production-quality Python services to wrap AI logic into secure microservices.
• Leverage AI coding assistants (Claude Code, Codex, Cursor, etc) to write the majority of your code, while still retaining ownership and deep understanding of the product created
• Own the model lifecycle, including versioning prompts as first-class code artifacts and monitoring for performance degradation.
• Manage the economics of LLM usage, balancing model performance against latency and cost

Collaboration & Technical Leadership

• Partner with Product, Data Science, and Backend teams to translate ambiguous requirements into technical specifications.
• Mentor junior engineers on AI craft, including embedding selection, vector store design, and prompt engineering precision.
• Actively reduce knowledge concentration by contributing to shared AI tooling and documentation.
• Contribute to roadmap planning and longer-term AI architecture decisions.

Platform Excellence & Innovation

• Establish and uphold standards for performance, security, privacy, and data governance within AI systems.

About You:

• Deep Technical Expertise: Extensive hands-on experience in software engineering and a strong understanding of the entire machine learning lifecycle
• Platform-Level Thinking: Proven ability to design and build scalable, distributed systems, ideally for machine learning or data-intensive applications
• MLOps Mastery: Demonstrated experience with MLOps tools and practices, including CI/CD for machine learning, model versioning, and feature stores
• Cloud Proficiency: Expertise with public cloud platforms (e.g., AWS, GCP, Azure) and a solid understanding of containerization and orchestration technologies like Docker and Kubernetes
• Data Fluency: A strong grasp of data engineering concepts, including data pipelines, data warehousing, and distributed data processing frameworks

Tech Stack:

• Cloud Platforms: Extensive experience with GCP and/or AWS, including core compute, storage, and networking services.
• Programming Languages: Expert-level proficiency in Python, familiarity with Go for backend services is a bonus.
• Data & AI Frameworks: Experience with big data processing platforms like Apache Spark, Apache Beam, Hadoop, etc
• Familiarity with modern machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
• MLOps & Orchestration: Deep understanding of MLOps principles and hands-on experience with tools like Kubeflow, MLflow, LangChain or LLamaIndex.
• Experience with workflow orchestration tools like Airflow or a similar platform.
• DevOps & Infrastructure:Expertise in containerization and orchestration using Docker and Kubernetes.
• Hands-on experience with Infrastructure as Code (IaC) tools like Terraform
• Data Systems: Experience with both relational and NoSQL databases, and familiarity with data warehousing and streaming technologies. Experience with vector databases (Pinecone, ChromaDB, Mongo) and retrieval strategies (chunking, hybrid search, re-ranking) a definite bonus

AI Fluency & Ways of Working

At League, we are an AI-native organization. We expect all employees regardless of role or level to thoughtfully leverage AI to improve the quality, speed, and impact of their work.

What this means in practice:

• Use AI tools as part of your daily workflow to enhance productivity, problem-solving, and decision-making (e.g., drafting, analysis, coding, research, or process automation)
• Apply judgment and accountability when using AI by reviewing outputs for accuracy, bias, and quality before use
• Continuously learn and adapt as new AI tools and capabilities emerge, incorporating them into your ways of working
• Identify opportunities to improve how work gets done from personal productivity to team-level workflows by leveraging AI effectively
• Operate with strong data responsibility and security awareness, especially when working with sensitive or regulated information

How this scales by level:

• Individual Contributors: Use AI to improve personal productivity and quality of output
• Senior ICs / Managers: Integrate AI into team workflows and improve processes
• Leaders: Drive AI adoption at the organizational level and shape how work is done across teams

What we look for:

• Demonstrated experience using AI tools in a practical, responsible way
• Curiosity and openness to experimenting with new technologies
• Ability to balance efficiency with quality and sound judgment

Security-related Responsibilities

• Ensure access management is performed in compliance with the employee's role and responsibilities
• Responsibility and accountability for executing League's policies and procedures within the department/ team
• Notification of HR, Legal, Compliance & Security of any incidents, breaches or policy violations
• Compliance with Information Security Policies