AI Engineer

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.


Required Qualifications
• Demonstrated experience developing and deploying AI-based solutions in production environments with measurable business or operational impact.
• Enterprise-scale experience building and deploying autonomous or agentic AI systems in production.
• Strong programming proficiency in Python and familiarity with TypeScript/JavaScript, Go, or similar programming languages.
• Strong understanding of artificial intelligence and modern AI/LLM development.
• Hands-on experience with context engineering, including agentic retrieval and search, memory architectures, enterprise data grounding, and structured outputs.
• Hands-on experience with agentic system design, including agent loops, multi-agent and sub-agent orchestration, and tool/function calling.
• Strong understanding of context window management and token budgeting, including cost and latency optimization for production workloads.
• Experience evaluating AI system quality, reliability, safety, and business impact.
• Solid understanding of software architecture and systems design, including API design, event-driven patterns, and scalable data modeling.
• Experience developing or deploying applications with large-scale impact, such as broad user adoption, high transaction volumes, or organization-wide implementations.
• Experience integrating AI solutions with multiple enterprise systems and platforms using REST/GraphQL APIs, CI/CD pipelines, cloud services, and enterprise tooling.
• Strong understanding of application and AI security, including secure development practices, security controls, guardrails, and enterprise security requirements.
• Experience with Security Harness Engineering or comparable security engineering practices for AI-enabled development.
• Demonstrated ability to work independently across the full delivery lifecycle with accountability for technical outcomes and business results.
• Strong communication and collaboration skills, with the ability to explain technical concepts to engineering, security, leadership, and business stakeholders.


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.