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United States
Remote only
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead AI/ML Engineer based in the United States.
This role offers the opportunity to lead the design and delivery of enterprise-grade AI solutions with a strong focus on production readiness and measurable business value.
You will work across generative AI, machine learning, retrieval-augmented generation, model serving, and AI orchestration.
The position combines hands-on engineering with technical leadership across data, platform, product, security, and business teams.
You will help build reliable AI systems that are accurate, explainable, secure, and scalable.
A key focus will be establishing robust evaluation, governance, and security practices for high-impact AI applications.
You will operate in complex enterprise environments where responsible AI and regulatory considerations are essential.
This is an opportunity to shape modern AI capabilities from architecture through deployment and continuous improvement.
Design, build, and optimize enterprise AI solutions, including RAG, semantic retrieval, model orchestration, and AI-powered decision-support capabilities.
Develop and maintain model-serving infrastructure, embedding services, vector search platforms, and inference pipelines for scalable production workloads.
Create AI evaluation frameworks and testing methodologies covering model accuracy, groundedness, response quality, reliability, and business performance.
Implement AI governance, security, and guardrail mechanisms, including PII protection, prompt-injection defenses, access controls, monitoring, and compliance controls.
Partner with data engineering teams to integrate governed data sources, semantic layers, and structured-query frameworks that support trustworthy and explainable AI outputs.
Collaborate with product, platform, security, engineering, and business stakeholders to define AI capabilities, improve performance, and continuously enhance solution effectiveness.
Provide technical leadership and help establish engineering practices that support reliable, maintainable, and production-ready AI systems.
6+ years of professional software engineering experience, including at least 2 years building and deploying production AI, machine learning, NLP, LLM, or RAG solutions.
Strong proficiency in Python and modern AI/ML frameworks, with demonstrated experience developing scalable, production-grade AI applications and services.
Hands-on expertise with LLM technologies, including model serving, vector databases, embeddings, prompt engineering, retrieval pipelines, agent and tool orchestration, and AI workflow automation.
Practical experience implementing AI governance and security controls, including model evaluation, guardrails, PII protection, prompt-injection prevention, access management, and responsible AI practices.
Strong software engineering fundamentals and the ability to design dependable AI systems that can operate at enterprise scale.
Excellent communication and collaboration skills, with the ability to work effectively across data, platform, product, security, engineering, and business functions.
Preferred experience with self-hosted or open-weight models, vLLM, Ollama, Qdrant, pgvector, LlamaIndex, Haystack, semantic layers, financial services, or other highly regulated industries.
Fully remote position within the United States.
Opportunity to lead enterprise-scale generative AI and machine learning initiatives.
Exposure to advanced technologies spanning LLMs, RAG, vector search, model serving, AI orchestration, and responsible AI.
Cross-functional collaboration with data, platform, product, security, engineering, and business teams.
Opportunity to shape AI architecture, governance, evaluation, and production engineering practices.
Work in complex enterprise environments where security, reliability, explainability, and compliance are core priorities.
Professional growth through ownership of technically challenging AI initiatives and emerging technologies.
