Senior R&D & AI Engineer

Option Care Health•Springfield, IL

1d•$112,440 - $187,407

About The Position

The Senior R&D & AI Engineer designs and develops advanced data processing pipelines and AI‑driven solutions on modern distributed platforms (e.g., PySpark, Palantir Foundry, or equivalent cloud frameworks). In addition to architecting large‑scale, real‑time systems, this role builds and deploys applications within Foundry to make insights and AI capabilities directly accessible to end users. By integrating machine learning models into production environments and collaborating across R&D, product, and infrastructure teams, the engineer transforms complex requirements into scalable, secure, and high‑performance applications that drive innovation in patient care and enterprise operations.

Requirements

Bachelor’s degree in computer science, Engineering, or related field required; Master’s degree (or higher) strongly preferred with emphasis on distributed systems, data engineering, or artificial intelligence
7–9 years of professional experience in application development, maintenance, and large‑scale system integration, with a proven track record of delivering production‑grade solutions.
5+ years of experience developing and writing detailed requirements specifications, technical designs, and system documentation for enterprise applications.
5+ years of hands‑on programming experience with modern software languages and frameworks relevant to data and AI engineering, such as: Python (for AI/ML, data pipelines, automation) Scala (for Spark and distributed data processing) SQL/T‑SQL (for advanced database development and optimization) R (a plus, for statistical modeling and advanced analytics)
5+ years of experience with software testing frameworks and tools, including automated testing, performance benchmarking, and CI/CD practices.
2+ years of experience with AI/ML platforms and integration (e.g., AIP, Palantir, or equivalent cloud‑native platforms).
Advanced SQL expertise (MS SQL, T‑SQL) including query optimization, stored procedures, triggers, indexing strategies, and performance tuning for large datasets.
Experience with distributed data processing frameworks (e.g., PySpark, Hadoop, or equivalent) and cloud platforms (AWS, Azure, GCP).
Familiarity with containerization and DevOps practices (Docker, Kubernetes, CI/CD pipelines).
Demonstrated ability to mentor junior engineers and contribute to technical standards, best practices, and architectural decision‑making.

Responsibilities

Architect, design, and implement advanced real‑time, large‑scale data processing and analytics systems on distributed platforms (e.g., PySpark, Palantir Foundry, or equivalent cloud‑native frameworks).
Develop and optimize distributed algorithms for large‑scale data integration, transformation, and analysis, applying advanced concepts in parallel computing, data modeling, and systems design.
Build and deploy user‑facing applications within Palantir Foundry (and similar platforms) to operationalize data pipelines and AI models, making insights and tools directly accessible to end users.
Integrate and operationalize AI/ML models into enterprise‑grade data ecosystems, ensuring reproducibility, scalability, and compliance with healthcare and industry regulations.
Evaluate AI/ML outputs using rigorous statistical methods, bias detection frameworks, and domain‑specific validation metrics to guarantee accuracy, fairness, and clinical/business relevance.
Prototype, benchmark, and productionize AI solutions, moving from research hypotheses to validated, production-ready systems with measurable performance improvements.
Collaborate with cross‑functional teams (R&D, product, DevOps, infrastructure, clinical/business stakeholders) to define system requirements, optimize data flows, and deliver against project milestones.
Implement CI/CD pipelines and observability practices for distributed data applications, ensuring automated testing, monitoring, and high-availability deployments.
Author and maintain technical documentation including system designs, data pipelines, application specifications, and deployment playbooks
Mentor engineers and establish best practices in distributed data engineering, AI integration, and scalable application development.
Continuously evaluate emerging technologies in AI, distributed computing, and cloud platforms (AWS, Azure, GCP, etc.), recommend adoption strategies that align with organizational goals.
Operate in compliance with OCH's data and AI governance frameworks