Senior AI & ML Engineer

CNA Financial; Chicago (IL); On-site; USD 150,000 - 210,000; Full time

Job summary

CNA Financial seeks an experienced Senior AI Platform Engineer to build and scale AI/ML platforms for financial services and insurance. You will own platform capabilities from discovery to production rollout, including LLM gateways, RAG pipelines, and automated evaluation tools.

The role emphasizes responsible AI, governance, and audit-ready infrastructures, with hands-on work on multi-agent workflows and hybrid retrieval systems. Chicago-based with enterprise-scale impact.

Qualifications

• Hands-on experience building enterprise AI platforms and ML pipelines.
• Experience with model serving, prompts, and RAG pipelines.
• Ability to implement trusted AI and governance.
• Experience with cloud-native distributed systems.
• Proficiency in Python.

Responsibilities

• Build and promote adoption of platform-wide standards, reusable design patterns, and golden-path templates that enable product and feature teams to build, deploy, and operate AI solutions safely and consistently.
• Drive end-to-end delivery of new platform capabilities, from technical discovery and design through prototyping, hardening, and full production rollout, with hands-on involvement at critical technical milestones to ensure quality and coherence.
• Build and operationalize the core platform service catalog, including LLM gateway and routing layers, prompt lifecycle management, agentic orchestration frameworks, Retrieval-Augmented Generation (RAG) pipelines, vector stores, model registries, and rigorous automated evaluation infrastructure.
• Build and maintain robust CI/CD and AIOps pipelines for AI systems, incorporating automated evaluation gates, model and data versioning controls, staged deployment promotion, and continuous cost and performance optimization guardrails.
• Implement enterprise-grade multi-agent and single-agent workflow patterns for high-value business use cases, following clear standards for orchestration design, state and memory management, tool and API integration, and safe autonomy controls, including human-in-the-loop approvals, permission scoping, and comprehensive audit trails.
• Design and implement knowledge grounding systems, including hybrid retrieval strategies, semantic reranking, ontology-driven entity modeling, and knowledge graph integration, to measurably improve AI output accuracy, explainability, traceability, and readiness for regulatory audit.
• Embed responsible AI and compliance-by-design principles across the platform, covering data privacy protections, enterprise secrets management, granular access controls, output leakage prevention, and model risk governance practices aligned to enterprise and regulatory standards.
• Participate in code and design reviews, and contribute to testing, documentation, and operational readiness.
• May perform additional duties as assigned.

Skills

AI platform design

AIOps engineering

Python programming

System reliability

Tools

LangGraph

Google ADK

Vertex AI

Cloud Run

GKE

Pub/Sub

BigQuery

Job description

You have a clear vision of where your career can go. And we have the leadership to help you get there. At CNA, we strive to create a culture in which people know they matter and are part of something important, ensuring the abilities of all employees are used to their fullest potential.

An individual contributor role that provides a high level of leadership and expertise in building Artificial Intelligence and Machine Learning (AI & ML) platforms and solutions for financial services and insurance. The role will be responsible for developing data into usable information to create automated analytics solutions.

JOB DESCRIPTION:

Essential Duties & Responsibilities

• Build, apply, and promote adoption of platform-wide standards, reusable design patterns, and golden-path templates that enable product and feature teams to build, deploy, and operate AI solutions safely and consistently.
• Drive end-to-end delivery of new platform capabilities, from technical discovery and design through prototyping, hardening, and full production rollout, with hands-on involvement at critical technical milestones to ensure quality and coherence.
• Build and operationalize the core platform service catalog, including LLM gateway and routing layers, prompt lifecycle management, agentic orchestration frameworks, Retrieval-Augmented Generation (RAG) pipelines, vector stores, model registries, and rigorous automated evaluation infrastructure.
• Build and maintain robust CI/CD and AIOps pipelines for AI systems, incorporating automated evaluation gates, model and data versioning controls, staged deployment promotion, and continuous cost and performance optimization guardrails.
• Implement enterprise-grade multi-agent and single-agent workflow patterns for high-value business use cases, following clear standards for orchestration design, state and memory management, tool and API integration, and safe autonomy controls, including human-in-the-loop approvals, permission scoping, and comprehensive audit trails.
• Design and implement knowledge grounding systems, including hybrid retrieval strategies, semantic reranking, ontology-driven entity modeling, and knowledge graph integration, to measurably improve AI output accuracy, explainability, traceability, and readiness for regulatory audit.
• Embed responsible AI and compliance-by-design principles across the platform, covering data privacy protections, enterprise secrets management, granular access controls, output leakage prevention, and model risk governance practices aligned to enterprise and regulatory standards.
• Participate in code and design reviews, and contribute to testing, documentation, and operational readiness.
• May perform additional duties as assigned.

Reporting Relationship

Typically Director or above

Skills, Knowledge & Abilities

• Deep AI platform and AIOps engineering expertise, with hands-on experience building, deploying, and operating shared AI platform capabilities such as model serving layers, LLM gateway and proxy services, prompt registries, vector databases, and automated evaluation harnesses at enterprise scale.
• Proven agentic system design capability, with hands-on experience building multi-agent and single-agent workflow systems using orchestration frameworks such as LangGraph and Google ADK, including tool and function calling patterns, state and memory persistence strategies, and robust safe autonomy controls.
• Applied GenAI depth spanning LLM solution architecture patterns, model selection and routing strategies, advanced prompt engineering techniques, fine-tuning and RLHF tradeoffs, and production-grade RAG and hybrid retrieval system design and optimization.
• Strong cloud-native and distributed systems skills, with deep GCP expertise across Vertex AI, Cloud Run, GKE, Pub/Sub, and BigQuery, and a solid command of API and service-based design, event-driven architecture, and high-availability and fault-tolerant system patterns.
• Knowledge grounding and semantic layer proficiency, including experience building canonical ontology and entity models, designing vector search and hybrid retrieval pipelines, integrating knowledge graphs, implementing reranking strategies, and establishing citation and traceability mechanisms that support explainability and compliance.
• Solid AIOps and platform reliability engineering experience, including CI/CD pipeline design for AI systems, automated evaluation and quality gates, model and dataset versioning, production monitoring and observability, reliability engineering practices, and systematic cost-performance optimization.
• Practical responsible AI and security expertise, with experience implementing enterprise AI governance frameworks, model risk management practices, PII and data privacy controls, audit and event logging, and compliance-by-design patterns suited to regulated industries.
• Strong SDLC and hands-on engineering fundamentals, including Python proficienc