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 DevSecOps 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.

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