Role Description We are seeking an Infrastructure as Code (IaC) Security Engineer to design, build, and maintain secure, scalable, and automated infrastructure solutions that underpin our AI security development platform. This role is responsible for owning the IaC layer across our container and orchestration stack—including EKS, Docker, and Helm—ensuring that all infrastructure is provisioned securely, repeatably, and in compliance with security best practices. The ideal candidate will embed security into every phase of infrastructure automation, from Terraform modules to CI/CD pipelines, enabling the AI security team to deliver rapidly without compromising the integrity of our environments. - Design, implement, and maintain secure Infrastructure as Code solutions for cloud and containerized environments supporting AI security workloads. - Own and manage EKS clusters, including node group configurations, networking policies, RBAC, and pod security standards to support secure AI model development and deployment. - Develop and maintain hardened Terraform modules, configurations, and reusable infrastructure patterns with built-in security controls (e.g., least-privilege IAM, encryption-at-rest, network segmentation). - Build and manage Docker images and Helm charts with security-first principles—image scanning, minimal base images, secrets management, and signed artifacts. - Integrate security guardrails into CI/CD pipelines, including automated policy checks (e.g., OPA/Gatekeeper, Checkov, tfsec) for infrastructure deployments. - Automate environment provisioning, scaling, configuration, and release processes with a focus on immutable infrastructure and drift detection. - Collaborate with AI security engineers, platform teams, and DevSecOps to ensure infrastructure supports threat modeling, vulnerability management, and incident response requirements. - Troubleshoot and remediate infrastructure security issues across Kubernetes, Terraform, CI/CD, and container platforms. - Enforce infrastructure compliance with organizational security policies, regulatory frameworks (e.g., NIST, CIS Benchmarks), and operational best practices. - Document secure infrastructure patterns, deployment runbooks, and automation workflows for the AI security development team. Qualifications - Bachelor’s degree preferred and/or equivalent relevant experience considered. - Strong hands-on experience designing and implementing secure Infrastructure as Code solutions in cloud and containerized environments. - Deep production experience managing Kubernetes, including EKS cluster administration, networking, RBAC, and workload security. - Strong experience with Terraform, including development of reusable modules and secure infrastructure provisioning patterns. - Hands-on experience building and managing Docker images and Helm charts for containerized deployments. - Experience integrating infrastructure automation into CI/CD pipelines with automated validation and deployment workflows. - Strong understanding of infrastructure security best practices, including IAM, encryption, secrets management, and network segmentation. - Experience troubleshooting and remediating issues across Kubernetes, Terraform, containers, and deployment pipelines. - Ability to collaborate effectively with engineering, platform, and DevSecOps teams in a fast-paced environment. Preferred Qualifications - Experience supporting AI, ML, or security-focused platform workloads in Kubernetes-based environments. - Experience with AWS and cloud-native services related to container orchestration, networking, and infrastructure automation. - Familiarity with infrastructure security and policy enforcement tools such as OPA/Gatekeeper, Checkov, tfsec, or similar solutions. - Knowledge of compliance and security frameworks such as NIST, CIS Benchmarks, or related infrastructure governance standards.

Principal AI Engineer - Agent Ops / SRE Locations (Hybrid) - Hartford, CT - Columbus, OH - Charlotte, NC - Chicago, IL 100% Remote USA Full time We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. The Hartford’s applied AI COE Team is seeking a Principal AI Engineer - Agent Ops/SRE. The AI-COE serves as a centralized function to accelerate AI maturity, eliminate silos, and streamline AI adoption through standardized processes and technologies, ensuring strong alignment with business objectives. AI-COE is responsible for building reusable AI solutions, horizontal AI Agents, Agent Starter Packs and partnering with other IT/Business unit to deliver and maintain their AI Assets. This role will support data scientist and AI solution engineer to build, deploy & maintain AI-COE products, ensuring reliability, uptime and throughput as per application tiers. This is the most senior individual contributor role, responsible for driving efficiency across the AI delivery lifecycle (AgentOps), while applying strong software and systems engineering practices to scale, operate, and ensure reliability of AI systems (SRE). You will work very closely with AI platform , Cloud Engineering, Security and Enterprise SRE team using their standards and tooling to deploy and maintain the solution. This role requires versatility across DevOps, MLOps, AgentOps, and SRE, with strong expertise in automation and a proven track record of building and operating large-scale, mission-critical systems. If you are passionate about automation and building large-scale distributed systems, we’d love to connect with you. Responsibilities: - Serve as technical liaison between AI COE and Platform Engineering & Enterprise SRE teams. - Ensure AI systems meet requirements for performance, latency, throughput, resiliency, recovery, observability and reliability. - Partner with AI engineers, Applied AI Scientist, and AI Architects to design, build and maintain scalable, fault tolerant AI systems as per SLO. - Partner with Platform engineering team to design and implement CICD, GITOps, and IAC (Terraform) modules. Making sure we use our AgentOps NSA, standards, Ref. architecture and tooling. - Partner with enterprise release management and AI Governance team to build & deploy AI solutions using their platform tooling. Supporting entire AI lifecycle as per the standard work template. - Build standardized deployment templates, reference architecture, automation scripts, terraform modules, CICD pipelines, and operational runbooks for AI workloads. - Design and build IDP (Harness) catalogs, templates & pipelines partnering with enterprise platform engineering team. - Manage production systems to ensure our enterprise SLOs are met. - Manage incident response for production systems, including triaging, escalating, RCA and implementing corrective actions. Qualifications: - Bachelor's degree in Computer Science, Computer Engineering, or a technical field. - 10+ years building and shipping software and/or platform solutions for enterprises. - Programming experience with Python is required. - 3+ years of experience with IAC (Terraform). - 5+ years of experience owning production CICD, GitOps and release management gating. - 3+ years of experience in implementing observability, performance & reliability solutions: SLO, P99-95 latency, alert tuning, & dashboards. - Experience with AI observability/monitoring tools such as Dynatrace, Splunk, Arize & OpenTelemetry/OpenInference is must. - Proven experience with Google's Gemini Enterprise Agent platform is a plus. - Experience with GKE/Docker/Registry is a plus. - Proven experience in working with other cloud providers such as AWS cloud is a plus. - Experience with Automated Testing, Automated Deployments, Agile methodologies, Unit Testing, and Integration Testing tools. - Conversational UX/UI design (multi-turn chatbots) and Human-Agent-Interaction (HAI) is a plus. - Experience with IR, vector embedding, and Hybrid/Semantic search technologies. - Experience with LLM orchestration frameworks like Langchain, LlamaIndex, LangSmith, LangGraph, Google Agent Development Kit, is a plus. - Experience with Generative AI Guardrails, responsible AI, adversarial attack mitigation, and red teaming is a plus. - Foundational understanding of Natural Language Processing and Deep Learning. - Excellent problem-solving skills and the ability to work in a collaborative team environment. - Excellent communication skills. This role can have a Hybrid or Remote work arrangement. Candidates who live near one of our office locations (Hartford, CT, Charlotte, NC, Chicago, IL, Columbus, OH) will have the expectation of working in an office 3 days a week (Tuesday through Thursday) Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise. Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position Compensation The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is: $168,400 - $220,000

• Build, ship, and own product features end-to-end • Work with designers, and product managers to create high-performing product features. • Apply a range of techniques—from classical ML to LLM-based approaches (RAG, prompt engineering, fine-tuning, semantic search)—with a strong focus on reliability, performance, and maintainability • Write well-designed, maintainable, and testable code • Write clear and well-defined design documentation • Troubleshoot, debug, and resolve software bugs • Be product-minded and think about the customer • Stay updated on AI/ML advancements and explore new techniques and tools. • Participate in an Agile software development life cycle • Work with Python, JavaScript, Node.JS, Docker, PostgreSQL, Kubernetes, etc

• Build and ship core product features, systems, and infrastructure. • Turn fast-moving, AI-assisted prototypes into reliable, scalable, production-grade software. • Own architecture, security, quality, and technical direction across major parts of the product. • Use tools like Codex, Claude Code, and modern AI coding agents to dramatically increase engineering output. • Direct, evaluate, and control agentic development workflows while maintaining strong human oversight. • Help define how the team builds: engineering standards, architecture patterns, review practices, and AI-native workflows. • Work closely with founders on product, strategy, technical tradeoffs, and execution. • Debug complex issues, make high-judgment technical decisions, and raise the overall engineering bar.