AI Engineer

Role overview

Responsibilities

• check_circle Design, deploy, and manage reusable AI capabilities within enterprise AI platforms, including skills, tools, and plug-in functionality.
• check_circle Drive the complete lifecycle of AI enablement solutions, from intake and configuration through deployment, governance, ongoing support, and optimization.
• check_circle Configure foundation models such as Claude, Gemini, and similar technologies to support enterprise automation and business workflows.
• check_circle Deliver AI solutions from concept through production implementation, ensuring scalability, security, and operational readiness.
• check_circle Integrate existing enterprise systems, services, and approved tools using available connectivity frameworks and protocols.
• check_circle Collaborate with Security, Cloud, and Infrastructure teams to implement appropriate access controls, identity management practices, and credential governance.
• check_circle Ensure solutions comply with established AI governance standards, including auditability, monitoring, risk controls, and operational guardrails.
• check_circle Partner with engineering, automation, data, and business teams to identify opportunities and deliver impactful AI-driven capabilities.
• check_circle Proven experience developing, publishing, and managing Claude Skills or plug-ins in a production environment.
• check_circle Demonstrated success deploying AI capabilities that are actively used by business stakeholders.
• check_circle Ability to contribute immediately with minimal onboarding and ramp-up time.
• check_circle Experience building and deploying technology solutions within regulated or highly governed enterprise environments.
• check_circle Strong understanding of security controls, compliance requirements, access management, and operational governance.
• check_circle Comfortable working within structured delivery processes and change-control frameworks.
• check_circle Strong communication and stakeholder management abilities.
• check_circle Capable of translating business challenges into practical AI-enabled solutions.
• check_circle Experience collaborating with both technical and non-technical audiences.
• check_circle 5+ years of experience in software engineering, automation engineering, or a related technical discipline.
• check_circle At least 2 years of experience designing and deploying production-grade solutions powered by large language models.
• check_circle Strong proficiency in Python development and API-based integrations.
• check_circle Experience with enterprise software integration patterns and distributed systems.
• check_circle Solid engineering practices, including testing, source control, observability, monitoring, and supportability.
• check_circle Hands-on experience with modern LLM ecosystems, including prompt engineering, model configuration, tool integration, and function execution.
• check_circle Experience working within public cloud environments such as Azure, Google Cloud Platform, Vertex AI, or equivalent technologies.
• check_circle Ability to evaluate AI use cases pragmatically and determine when traditional engineering approaches may be more effective.
• check_circle Self-directed and capable of independently leading technical initiatives in a fast-moving environment.

Preferred qualifications

• Experience building AI agents and multi-agent workflows.
• Familiarity with orchestration platforms and agent frameworks.
• Understanding of Model Context Protocol (MCP) implementations and agent-to-agent integrations.
• Experience with RAG architectures, vector databases, embeddings, and semantic search solutions.
• Knowledge of modern identity and access management concepts, including RBAC, service accounts, agent identities, and least-privilege models.
• Previous experience supporting organizations operating within highly regulated industries such as insurance, financial services, healthcare, or similar sectors.