Enterprise AI Engineer: Secure, Agentic Systems & LLMOps

Job Summary
We are seeking an AI Engineer with demonstrated experience delivering enterprise-scale AI solutions from concept through production. The ideal candidate will have hands-on experience building and deploying autonomous and agentic AI systems, with strong expertise in Python, artificial intelligence, security, architecture, and production engineering. This role requires end-to-end ownership, strong understanding of token optimization, secure-by-design practices, and the ability to translate business requirements into scalable AI solutions with measurable business outcomes.

Key Responsibilities

• Design, build, and deploy AI-powered capabilities across the Software Development Lifecycle (SDLC).

• Build and deploy autonomous and agentic AI systems for enterprise-scale production environments.

• Develop Spec Driven Development workflows that translate well-formed specifications into secure and verifiable implementations.

• Implement guardrails, policy enforcement, and verification mechanisms for AI-generated code and AI-assisted development.

• Develop developer-assist and verification capabilities that perform automated security checks, including design reviews, dependency and software supply-chain analysis, static and dynamic analysis orchestration, and release audit support.

• Integrate AI solutions with enterprise systems including source control, CI/CD, ticketing, security scanning, identity platforms, and internal applications using APIs, webhooks, and protocols such as MCP (Model Context Protocol).

• Partner with engineering, security, product, and leadership stakeholders to define requirements, evaluate technical trade-offs, and support solution adoption.

• Apply sound architecture and systems design practices, including service boundaries, data modeling, secure defaults, observability, and extensibility.

• Design and operate agentic systems responsibly and efficiently, including agent loops, sub-agent orchestration, context management, and token budgeting.

• Optimize AI workloads for cost, latency, token consumption, performance, and scalability.

• Evaluate emerging AI technologies including agentic frameworks, tool use, agentic retrieval, memory systems, structured outputs, evaluation frameworks, and LLMOps.

• Establish evaluation and quality practices for AI outputs, measuring accuracy, reliability, safety, and business impact.

• Apply secure-by-design and secure-by-default practices throughout AI solution development and deployment.

• Contribute to team enablement through documentation, demonstrations, reusable patterns, mentoring, and knowledge sharing.

• Demonstrate end-to-end ownership across requirements analysis, architecture, implementation, deployment, adoption, and ongoing improvement.

Preferred Qualifications

• Experience with Dev Sec Ops  practices.

• Experience with threat modeling and application security.

• Experience designing and implementing AI security guardrails and policy enforcement mechanisms.

• Experience with MCP (Model Context Protocol) integrations.

• Experience with LLMOps and AI evaluation frameworks.

• Experience with AI agent frameworks, agentic retrieval, and memory systems.

• Experience developing reusable AI engineering patterns and enterprise enablement frameworks.