Lead AI Engineer

TL;DR

Lead AI Engineer (AI/LLMs): Architect, build, and lead end-to-end AI solutions across enterprise use cases with an accent on generative AI, agentic AI, and LLM-based systems. Focus on rigorous evaluation and benchmarking, production scaling with orchestration/monitoring, and translating results into clear insights for senior leadership.

Company

hirify.global is seeking a Lead AI Engineer to design and scale advanced AI solutions for enterprise and life insurance use cases.

What you will do

• Architect, build, and lead end-to-end AI solutions from ideation through production deployment and monitoring using LLMs, agentic AI, machine learning, and probabilistic modeling.
• Design and conduct rigorous evaluations of AI performance, including experimentation, benchmarking across foundation models, and quantitative analysis.
• Drive innovation by translating emerging research into practical applications and establishing team-wide best practices for responsible AI deployment.
• Build rapid prototypes and deliver production-grade AI-powered applications (e.g., intelligent interfaces, dashboards, automated workflows) when viable.
• Collaborate with engineering teams to build production-grade AI pipelines and APIs integrated into the enterprise technology ecosystem.
• Mentor junior talent and communicate AI insights and technical decisions effectively to senior leadership.

Requirements

• Location: Springfield, Massachusetts; Boston, Massachusetts; or New York, New York
• 7+ years of experience in data science, machine learning, or AI engineering with a track record of delivering AI/ML solutions at scale.
• Deep expertise in machine learning, statistics, NLP, and LLMs, including generative AI, agentic architectures, prompt engineering, and LLM evaluation across foundation models and benchmarks.
• Proven ability to build, deploy, and scale production AI systems from architecture planning through orchestration, monitoring, and end-user delivery.
• Strong Python programming skills and experience with Docker and Kubernetes (or similar orchestration/deployment frameworks).
• Exceptional communication skills to translate complex AI concepts and quantitative findings for non-technical stakeholders and senior leadership.

Nice to have

• Experience with agentic AI tooling ecosystems (e.g., AWS Bedrock AgentCore, AWS Strands, Azure, MCP/A2A protocols).
• Experience developing and evaluating AI systems in regulated industries with strong compliance, privacy, and responsible AI practices.
• Breadth across AI/data science methods (e.g., causal inference, optimization, Bayesian approaches) and comfort moving across techniques.
• Proficiency in SQL and database design; familiarity with cloud-native data platforms, vector databases, and semantic search.

Culture & Benefits

• Cross-functional work with applied AI, statistics, and computer science experts.
• Accountability for reliability, performance, and maintainability of production AI systems.
• Emphasis on scientific rigor, continuous learning, and technical excellence through mentoring.
• Opportunity to influence enterprise strategy by communicating AI insights to senior leadership.

Hiring process

• Interviews and evaluation focused on AI/ML engineering experience, production scaling, and evaluation/benchmarking approach.
• Discussion of technical decisions and communication of results to senior stakeholders.