AI Engineer

Location: Indianapolis

# AI Engineer

AI Engineer Salary $120000 - $160000

Location Indianapolis, IN, USA---Contact Jessie Connolly Je
** Responsibilities
* * In this role, you will:
** Production AI & Prototyping:
** Improve and expand existing AI/analytics platforms while designing new solutions through research, experimentation, and rapid prototyping to address complex public-sector healthcare challenges.
** Model Development & Explainability:
** Build interpretable, defensible machine learning models and statistical methods that integrate into user workflows, including feature attribution, benchmarking, and structured output summaries.
** Client Deliverables & Communication:
** Develop clear, client-ready reports, exhibits, and presentations that communicate methodology, findings, limitations, and actionable recommendations to non-technical audiences.
** Governed Generative AI:
** Design and implement governed GenAI solutions, including prompt engineering, RAG frameworks, and agent-based workflow orchestration, with strong evaluation standards and traceability for regulated environments.
** Operational ML Performance:
** Take ownership of model performance by defining success metrics, monitoring data quality and drift, tuning thresholds, and building dashboards for triage and KPI tracking.
** Business Development Support:
** Contribute to proposals and RFPs by drafting technical approaches, outlining methodologies, developing solution diagrams, and participating in capability demonstrations.
*
* Cross-Functional Collaboration:

** Work closely with actuarial, clinical, pharmacy, and policy SMEs to translate business needs into measurable solutions and ensure outputs align with real-world operational decisions.
** Engineering Excellence:
** Partner with data engineering to build scalable, production-ready pipelines with strong quality controls, reproducibility, logging, and documentation standards.

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** Qualifications
* *** Executive-Level Communication:
** Ability to produce polished client-facing reports and presentations and clearly articulate tradeoffs, risks, and limitations to non-technical stakeholders.
** Applied ML & Statistical Expertise:
** Strong hands-on experience applying machine learning to large-scale datasets, leveraging interpretable techniques, comparative analytics, and defensible anomaly detection suitable for regulated review.
** End-to-End ML Delivery:
** Proven track record of leading projects from ambiguous problem definition through deployment and adoption in operational environments.
** Healthcare Data

Experience:

** Direct experience working with healthcare datasets (claims and encounters preferred), including feature engineering, validation, and explainability within audit and oversight contexts.
** Applied Generative AI:
** Practical experience with prompt engineering, API integrations, and governed retrieval systems (RAG) with structured evaluation and guardrails.
** Production Engineering Discipline:
** Advanced Python skills in collaborative codebases (OOP, modular design) with strong engineering practices such as testing, code reviews, structured logging, and Git-based workflows.
** Data at Scale:
** Strong SQL and distributed data processing experience (Spark, PySpark, Databricks preferred), with a focus on data quality, validation, and performance optimization
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