Lead AI Engineer

Job description

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world! Job Description Design, build, and scale enterprise-grade AI/GenAI solutions that automate business processes, enhance decision-making, and deliver measurable business outcomes. The role focuses on developing AI-powered applications, intelligent workflows, and integrations by combining enterprise systems, data, APIs, and modern AI technologies in a secure, scalable, and production-ready environment. Responsibilities Design, develop, and deploy AI/GenAI applications, RAG pipelines, and intelligent workflow automation Build backend services, APIs, integrations, and orchestration frameworks using LLMs, model APIs, and enterprise data. Develop AI-powered knowledge assistants, workflow automation, and decision support solutions. Leverage AI-assisted development while ensuring code quality, security, reliability, observability, and governance. Define evaluation frameworks to measure AI quality, accuracy, latency, and system performance. Collaborate with business, IT, Security, Data, HR, and Finance teams to identify and deliver high-value AI use cases. Prototype rapidly and scale solutions into secure, production-ready deployments. Optimize AI solutions for performance, scalability, reliability, and cost. Develop reusable AI components, frameworks, and best practices to accelerate enterprise adoption. Required Qualifications 7–12+ years of experience in Software Engineering, AI/ML, or GenAI application development. Proven experience building and deploying production-grade AI/GenAI solutions. Strong expertise in Python, backend development, APIs, system integration, and workflow orchestration. Hands-on experience with RAG, LLMs, prompt engineering, tool/agent calling, AI evaluation, and observability. Experience integrating enterprise applications, data platforms, and services using APIs and event-driven architectures. Strong analytical, systems thinking, and problem-solving skills with the ability to translate ambiguous business problems into scalable AI solutions. Preferred Qualifications Experience with vector databases, embeddings, semantic search, and advanced RAG architectures. Knowledge of SQL, data engineering, and data pipelines. Experience integrating AI into enterprise applications and business workflows. Exposure to intelligent agents, automation platforms, and AI governance frameworks. Experience building reusable AI frameworks, accelerators, or enterprise AI platforms.