Senior AI & ML Engineer

CNA Insurance • Chicago, IL

19h • $72,000 - $141,000 • Hybrid

About The Position

This individual-contributor role provides leadership and technical expertise in building AI and machine learning platforms and solutions for financial services and insurance. The engineer develops data into usable information to create automated analytics solutions and helps ensure employees’ abilities are used to their fullest potential.

The role builds shared platform capabilities, standards, and reusable patterns; delivers capabilities from discovery through production rollout; and develops AI services and workflows, including LLM routing, prompt management, agent orchestration, RAG, vector stores, model registries, and automated evaluation. It also focuses on reliable AI operations, knowledge grounding, responsible AI, security, compliance, and production readiness.

The position calls for extensive software engineering and AI/ML platform experience, strong Python and GCP skills, and the ability to collaborate with technical, product, and business stakeholders. A bachelor’s degree in a related field or equivalent is required; a relevant master’s degree is strongly preferred.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or equivalent.
• 8+ years of software engineering and technical delivery experience, including large-scale distributed enterprise systems across the full SDLC.
• 5+ years of hands-on experience designing, delivering, and operating AI/ML or GenAI platform capabilities in production, with measurable outcomes in quality, reliability, and developer adoption.
• Strong Python proficiency and deep practical GCP experience, including Vertex AI, GCP Agent Builder, and Gemini.
• Hands-on AI platform and AIOps engineering experience, including shared enterprise-scale capabilities such as model serving, LLM gateways, prompt registries, vector databases, and automated evaluation harnesses.
• Experience designing agentic systems, including multi-agent and single-agent workflows, tool and function calling, state and memory persistence, and safe autonomy controls.
• Applied GenAI expertise, including LLM architecture, model selection and routing, prompt engineering, fine-tuning and RLHF tradeoffs, and production RAG and hybrid retrieval.
• Strong cloud-native and distributed systems skills, including GCP services such as Vertex AI, Cloud Run, GKE, Pub/Sub, and BigQuery; API and service design; event-driven architecture; and high-availability and fault-tolerant patterns.
• Experience with knowledge grounding and semantic layers, including ontology and entity models, vector and hybrid retrieval, knowledge graphs, reranking, and citation and traceability mechanisms.
• Experience with AI system CI/CD, automated evaluation and quality gates, model and dataset versioning, production monitoring and observability, reliability engineering, and cost-performance optimization.
• Experience implementing AI governance, model risk management, PII and data privacy controls, audit and event logging, and compliance-by-design practices for regulated industries.
• Strong SDLC and hands-on engineering fundamentals, including architectural and code reviews, AI testing strategies, technical debt management, refactoring, and operational readiness.
• Familiarity with Scaled Agile (SAFe), including contributing to Enabler Epics and PI planning.
• Strong communication and collaboration skills, including negotiating technology tradeoffs, mentoring junior engineers, and explaining technical concepts to non-technical audiences.

Nice To Haves

• Master’s degree in AI, Machine Learning, Data Science, or a related discipline.
• Prior experience in regulated industries such as insurance, financial services, or healthcare.
• Consulting or enterprise delivery background.
• Experience with orchestration frameworks such as LangGraph and Google ADK.

Responsibilities

• Build, apply, and promote platform-wide standards, reusable design patterns, and templates for safely and consistently building, deploying, and operating AI solutions.
• Drive delivery of platform capabilities from technical discovery and design through prototyping, hardening, and production rollout, providing hands-on technical support at critical milestones.
• Build and operationalize platform services, including LLM gateways and routing, prompt lifecycle management, agent orchestration, RAG pipelines, vector stores, model registries, and automated evaluation infrastructure.
• Build and maintain CI/CD and AIOps pipelines for AI systems, including automated evaluation gates, model and data versioning, staged deployment promotion, and cost and performance optimization.
• Implement multi-agent and single-agent workflows, including orchestration, state and memory management, tool and API integration, human approvals, permission scoping, and audit trails.
• Design and implement knowledge grounding systems using hybrid retrieval, semantic reranking, ontology-driven entity models, and knowledge graph integration to improve accuracy, explainability, traceability, and audit readiness.
• Embed responsible AI and compliance-by-design practices, including data privacy protections, secrets management, granular access controls, output leakage prevention, and model risk governance.
• Participate in code and design reviews and contribute to testing, documentation, and operational readiness.
• Perform additional duties as assigned.

Benefits

• Comprehensive and competitive benefits package intended to support employees and their family members’ physical, financial, emotional, and social wellbeing.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior