Role Description This is a high-impact technical leadership role within the Optum Tech AI Transformation team, supporting key enterprise initiatives such as the Real-Time AI Underwriting platform. You will own the technical direction and people leadership for a squad of AI/ML engineers, driving strategic engineering efforts. Beyond building, you will lead, mentor, govern, and ensure that everything your team delivers meets the highest standards of quality, compliance, and Responsible AI. You'll serve as a bridge between engineering execution and leadership vision, ensuring AI investments translate into measurable, production-grade business outcomes. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. - Design, develop, and deploy production-grade AI-powered solutions—spanning classical ML, Generative AI (LLMs, RAG, Graph RAG), and multi-agent orchestration frameworks—to address complex business challenges with emphasis on responsible and secure AI practices. - Own the end-to-end AI/ML technical roadmap, defining architectural direction, choosing optimal technologies, and establishing engineering and coding standards for the team's portfolio. - Use enterprise-approved AI tools to streamline workflows, automate tasks, and drive continuous improvement across the development lifecycle, optimizing team productivity and operational efficiency. - Evaluate emerging trends and technologies (such as Agentic AI, foundation models, fine-tuning, and enterprise AI tooling) to inform high-quality solution design and strategic innovation. - Drive MLOps maturity across development pipelines, overseeing model versioning, CI/CD for ML, retraining automation, drift detection, and production monitoring. - Ensure compliance and ethical governance by aligning solutions with enterprise policies, Responsible AI principles, and data privacy regulations (HIPAA/PHI handling). - Lead, mentor, and develop a team of 5–10 direct/indirect AI/ML engineers, setting clear goals, conducting regular 1:1s, and fostering a high-performance, self-managed culture. - Own and conduct robust technical reviews (architecture, design, and code reviews) to enforce rigorous standards, security posture, and pythonic best practices. Qualifications - 10+ years of software development experience with a strong engineering and architecture foundation. - 2+ years of hands-on experience in AI/ML project implementation (full lifecycle from ideation, data analysis, model development through production deployment). - Hands-on experience with NLP (text processing, embeddings, transformers, NER, text classification, or conversational AI). - Working experience with GenAI (RAG pipelines, prompt engineering, LLM integration, fine-tuning techniques like LoRA, and enterprise deployment). - Experience or strong working knowledge of Agentic AI development (agentic workflows, tool orchestration, multi-agent systems, or planning frameworks). - Solid Python programming skills with deep proficiency in production-grade coding, code reviews, and enforcing standards. - Driver’s License and access to a reliable transportation. Preferred Qualifications - Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field (or equivalent professional experience). - Experience with MLOps tooling (MLflow, Weights & Biases, Kubeflow, etc.) for experiment tracking, model registry, and automation. - Familiarity with Responsible AI frameworks (bias detection, fairness metrics, model explainability like SHAP/LIME, and adversarial red-teaming). - Knowledge of Graph RAG, knowledge graphs, and ontology-based reasoning systems. Benefits - Comprehensive benefits package. - Incentive and recognition programs. - Equity stock purchase. - 401k contribution (all benefits are subject to eligibility requirements). Application Deadline This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.

Role Description We are seeking a hands-on Senior AI/ML Engineer (IC) to join our AI Transformations team within Optum Technology. In this remote role, you will design, build, and deploy scalable AI solutions across the enterprise, focusing on deep technical execution and solving complex business problems using Data Science, Classical ML, NLP, and Generative AI. Working across the full AI lifecycle—from ideation to production - you will partner with engineers, product teams, and business stakeholders to translate use cases into high-impact AI solutions. You will have the opportunity to build advanced capabilities like agentic workflows, multi-step orchestration, and Retrieval-Augmented Generation (RAG) pipelines to drive strategic enterprise transformations. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. - Design, develop, and deploy end-to-end, scalable AI/ML solutions and pipelines—from problem definition and data ingestion to production deployment and monitoring - with a strong emphasis on the responsible use of AI. - Build and productionize ML models and pipelines, incorporating feature engineering, model training, evaluation, and inference services. - Use enterprise-approved AI tools and frameworks to streamline development workflows, automate engineering tasks, and drive continuous improvement. - Implement scalable AI systems following software engineering and MLOps best practices, including CI/CD, versioning, monitoring, and observability. - Develop NLP, Generative AI, and Agentic solutions, utilizing embeddings, transformers, RAG pipelines, prompt engineering, and tool orchestration. - Apply classical ML and statistical techniques to solve predictive and analytical business challenges. - Evaluate emerging technology trends to inform strategic innovation and solution design. - Collaborate with data engineers, product teams, and architects to align AI solutions with business objectives, and mentor junior engineers through code reviews and design discussions. Qualifications - 8+ years of software engineering experience. - 2+ years of experience in AI/ML project implementation, covering the end-to-end lifecycle from ideation to production deployment. - Demonstrated professional experience writing production-grade Python code. - Experience with classical AI/ML algorithms and techniques (e.g., regression, classification, clustering, ensemble models like Random Forest or XGBoost). - Hands-on experience with Natural Language Processing (NLP) tasks (e.g., embeddings, Named Entity Recognition (NER), or text classification). - Experience with Generative AI frameworks, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and model APIs. - Driver’s License and access to a reliable transportation. Preferred Qualifications - Experience in the Healthcare or Insurance domain (e.g., claims, underwriting, clinical workflows). - Experience with MLOps tools and practices (such as MLflow, CI/CD pipelines, model monitoring, and observability). - Solid understanding of Responsible AI principles, bias detection, and model governance. Benefits - Comprehensive benefits package. - Incentive and recognition programs. - Equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). Application Deadline This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.

• Lancer et développer les activités commerciales sur le marché français • Identifier, qualifier et conclure de nouvelles opportunités commerciales • Développer et entretenir des relations solides avec les acteurs clés du secteur • Gérer l’ensemble du cycle de vente, de la prospection à la signature des contrats • Accompagner l’onboarding des nouveaux clients • Représenter l’entreprise lors d’événements professionnels

• Design and build AI-enabled features for various education products, from assessments to tutoring. • Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints. • Build evaluation approaches that measure output quality, consistency, usefulness and failure modes. • Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate. • Integrate LLM APIs, AI services and education data into reliable product capabilities. • Design practical human-review, control and escalation workflows for AI-supported decisions. • Improve the reliability, latency, cost and quality of AI services in production. • Build prototypes quickly, test them against real education use cases and productionise successful approaches. • Develop suitable guardrails, monitoring and auditability for AI outputs. • Work with Backend Engineers on service architecture, data handling, integrations and scalability. • Work with Product Engineers to ensure AI features are understandable, useful and trustworthy for teachers and students. • Use AI-assisted development tools appropriately while maintaining strong engineering judgement, testing, privacy and security standards. • Write clean, tested and maintainable code.