Senior AI Machine Learning Engineer
The Hartford18 days ago
Chicago, IL, USA +3 moreSenior
Base Salary
$117k - $176k/yr
Responsibilities
- Lead engineering execution for predictive model portfolios supporting pricing and underwriting, including scoring, refreshes, monitoring, validation, and production support.
- Build, deploy, and maintain AI/ML components and data pipelines across pricing, underwriting, sales, service, renewal, and policy lifecycle workflows.
- Translate approved architecture and solution designs into tested, reliable, production-ready code and workflows.
- Support generative AI and agentic AI solutions involving prompt orchestration, retrieval-augmented generation, evaluation workflows, guardrails, and ecosystem integration.
- Develop and operate batch and near-real-time pipelines for training, feature generation, inference, post-processing, business rules integration, and downstream consumption.
- Deploy and sustain production AI services, jobs, APIs, and workflows in AWS and GCP.
- Perform code reviews, testing, documentation, runbook creation, production readiness checks, and incident response support.
- Guide and mentor junior engineers and partner with data scientists, data engineers, asset owners, pricing teams, and underwriting stakeholders.
- Maintain model and pipeline governance artifacts, identify operational gaps, and recommend improvements.
Requirements
- Bachelor’s degree in a related field or six-plus years of equivalent experience in software engineering, data engineering, ML/DevOps engineering, applied AI engineering, or closely related technical roles.
- Strong hands-on expertise in Python, SQL, Git-based development, automated testing, SDLC practices, and production-grade code delivery.
- Experience deploying and operating data, AI, or ML workloads in AWS and GCP, including storage, managed compute, orchestration, access patterns, logging, and monitoring.
- Experience with feature pipelines, model training, batch scoring, inference services, model monitoring, drift detection, validation, retraining, and production support.
- Ability to work within enterprise architecture, security, governance, coding, and operational standards.
- Ability to lead implementation work, guide junior engineers, communicate tradeoffs, and manage multiple deliverables with limited day-to-day direction.
- Preferred qualifications include a master’s degree in computer science, engineering, information technology, MIS, data science, or a related discipline.
- Preferred experience includes insurance or employee benefits analytics, governed predictive model portfolios, generative or agentic AI, RAG, prompt evaluation, LLM application integration, AI safety controls, human-in-the-loop workflows, orchestration tools, CI/CD, containers, APIs, infrastructure-as-code, observability, and production incident management.
Benefits
- Hybrid work schedule with an expectation of working in an office three days per week.
- The compensation package may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition in addition to base pay.
Tech Stack
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