Senior Decision Intelligence Engineer (NBA)

Become a part of our caring community   Become a part of our caring community and help us put health first. We are looking for a skilled Decision Intelligence Engineer to design, train, and improve the reinforcement learning policy at the heart of Humana's Next Best Action platform. This role is hands-on and research-oriented. You will design and evaluate decision-making algorithms, and instrument training pipelines. Additionally, you will collaborate with data and platform engineers. Furthermore, you will ensure the system operates correctly within the constraints of clinical eligibility rules and program-specific objectives. The Senior Decision Intelligence Engineer Is involved in all stages of software development, including front-end development, back-end development, database integrations, network and hosting management, user interface, user experience, and back-end server management. Begins to influence department’s strategy. Makes decisions on moderately complex to complex issues regarding technical approach for project components, andwork is performed without direction. Exercises considerable latitude in determining objectives and approaches to assignments. Use your skills to make an impact   Required Qualifications 5+ years (post undergraduate level) of software engineering or quantitative research experience building and operating large-scale production systems, with emphasis on data-intensive platforms, recommendation systems, optimization engines, or simulation frameworks serving millions of users. 2+ years (post graduate level) of software engineering or quantitative research experience building and operating large-scale production systems, with emphasis on data-intensive platforms, recommendation systems, optimization engines, or simulation frameworks serving millions of users. 2+ years of hands-on experience implementing reinforcement learning, operations research methods, or simulation-driven decision systems in production. Relevant backgrounds include policy gradient and value-based RL (PPO, A3C, DQN, CQL), stochastic dynamic programming, discrete-event simulation, or large-scale combinatorial or constrained optimization. Deep familiarity with Markov Decision Processes, Bellman-equation-based value estimation, reward or objective shaping, exploration-exploitation tradeoffs, and constraint formulation in real-world decision systems. Demonstrated ability to diagnose failure modes in learned or optimized policies: instability, poor credit assignment across long horizons, and distributional shift across large populations. Proficiency in Python 3.x; experience with PyTorch or TensorFlow for policy network or learned model implementation. Experience with Ray RLlib or equivalent distributed computation frameworks for large-scale training or optimization. Experience with Databricks, PySpark, and Delta Lake for large-scale ML or data pipelines processing tens of millions of records. Experience with MLflow for experiment tracking, model registry, and artifact management. Experience with shipping systems that operate reliably under production load, not just research or prototype work.

Experience with multi-agent RL frameworks (PettingZoo or equivalent) or multi-agent simulation and coordination methods. Familiarity with operations research methods applicable to constrained sequential decisioning: linear programming, mixed-integer programming, Lagrangian relaxation, or constraint programming. Experience operating decision or optimization systems in regulated domains (healthcare, finance, or insurance) where member safety, auditability, and explainability are requirements. Experience building simulation environments using Gymnasium, SimPy, AnyLogic, or equivalent frameworks for policy evaluation and backtesting. Familiarity with event-driven feedback loops and how disposition signals feed retraining or re-optimization pipelines. OpenTelemetry instrumentation experience for ML or