Advance global health optimization by architecting and delivering scalable AI/ML solutions. This role focuses on integrating intelligent capabilities into healthcare applications, building cloud-native services, and developing robust APIs.

You will leverage cutting-edge AI technologies like generative AI, RAG, and AI agents to automate workflows and enhance decision-making. Utilizing AI-powered development tools, you'll accelerate delivery and establish automated pipelines for model lifecycle management, ensuring security, reliability, and responsible AI practices.

As a principal technical leader, you will guide engineering decisions, mentor fellow engineers, and drive software modernization. Your work will transform promising AI/ML concepts into reliable, maintainable production solutions that deliver measurable business impact.

Translate business needs into quantifiable ML tasks, validate data, establish baselines, and integrate appropriate models to prove AI's impact on outcomes.

Integrate generative AI and LLMs where they offer significant value, utilizing RAG for contextual relevance and meticulously evaluating accuracy, unsupported claims, latency, and costs.

Develop model-serving interfaces and MLOps workflows for comprehensive versioning, deployment, monitoring, and rollback strategies.

Monitor data and model performance, implementing improvements to models, prompts, or product design based on observed changes.

Apply responsible AI practices through documented evaluations, bias and robustness checks, privacy safeguards, and human review, while addressing security concerns like prompt injection and data leakage.

Design and build distributed services, APIs, and reliable data integrations, making informed trade-offs regarding scalability, performance, cost, and maintainability.

Write and review production code, automate testing, and build CI/CD pipelines for secure, repeatable cloud releases.

Embed security and privacy into software design using access controls, secrets management, and safe data handling, proactively identifying and mitigating technical risks.

Own production readiness and service health via logs, metrics, and traces, actively troubleshooting incidents and enhancing reliability.

Lead cross-team architectural decisions, deliver reusable components, document trade-offs, mentor engineers, and break down complex requirements into manageable releases.

Possess a Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or demonstrate equivalent practical experience.

Bring a minimum of 15 years of relevant engineering experience, including at least 8 years in hands-on software engineering and 3 years dedicated to delivering AI/ML capabilities in production environments.

Have a proven track record of taking ML or generative AI capabilities from initial evaluation through to production, encompassing model integration, deployment, monitoring, and measurable quality assessment.

Exhibit strong coding proficiency in at least one production backend language, with functional command of Python specifically for AI/ML development.

Demonstrate experience in delivering distributed systems, APIs, data integrations, automated tests, and cloud deployments, coupled with practical security and operational ownership.

Possess the ability to articulate technical decisions clearly, influence architectural direction across teams, and mentor engineers while actively contributing production code.