Lead AI Engineer

We are seeking a hands-on, collaborative Lead AI Engineer to drive the technical direction, architecture, and delivery of GenAI-powered solutions, agentic workflows, and model integration across our platform. As Lead AI Engineer, you will provide technical leadership and guidance to the AI engineering team, ensuring the delivery of robust, scalable, and responsible AI capabilities. This role partners closely with data, product, and engineering teams to deliver intelligent, reliable, and ethical solutions.

Key Responsibilities

• Provide technical leadership, mentorship, and guidance to the AI engineering team

• Lead the architecture, design, and implementation of GenAI-powered solutions and agentic workflows

• Drive prompt engineering, model selection, and integration of external and self-hosted models

• Champion responsible AI practices, including bias detection, explainability, and ethical AI

• Architect and maintain agent memory, feedback loops, and evaluation engines for continuous learning

• Ensure robust, scalable, and reliable AI and agentic workflows

• Oversee model efficacy, quality assessment, and continuous improvement

• Collaborate closely with data, product, and engineering teams to deliver integrated solutions

• Lead the selection and adoption of modern AI/ML tools, frameworks, and best practices

• Ensure engineering rigor, code quality, and documentation standards are met

• Facilitate clear communication, knowledge sharing, and effective documentation within the team

• Support team growth through coaching, feedback, and skills development

Additional Responsibilities

• Proactively track, evaluate, and chronicle advancements in AI models, tooling, software, and partner ecosystems; share insights and recommendations with the organization to drive innovation and alignment.

• Collaborate with QA/SDET teams to ensure comprehensive test coverage, efficacy, and continuous improvement of AI solutions.

• Work closely with operations and observability teams to ensure effective use of systems feedback for model and workflow improvement, reliability, and monitoring.

Core Qualities & Skills

• Proven experience leading AI/ML engineering teams and delivering complex projects (including recent GenAI/LLM-powered solutions)

• Demonstrated hands-on experience designing, building, or integrating GenAI/LLM powered solutions (even if recent)

• Ability to rapidly learn, adapt, and apply emerging GenAI technologies and best practices

• Deep expertise in AI/ML, NLP, or data engineering, with a proven ability to transfer skills to GenAI

• Strong programming skills in Python and TypeScript.

• Experience with AI/ML model evaluation, quality assessment, and continuous improvement

• Experience with responsible AI practices (bias detection, explainability, ethical AI)

• Strong understanding of agent memory, feedback loops, and evaluation engines

• Experience with data engineering, analytics, and observability for AI systems

• Strong collaboration and alignment skills across disciplines

• Willingness to learn, share knowledge, and adapt to evolving technologies

• System design skills and awareness of technical debt and tradeoffs
• Excellent communication, documentation, and stakeholder management abilities

• Comfort with ambiguity, discovery, and rapid change

• Commitment to engineering excellence, security, and responsible practices

Preferred Qualifications

• 7+ years of hands-on software engineering experience

• 5+ years of hands-on AI/ML engineering experience, with recent (12–24 months) practical experience in GenAI, LLMs, or agentic workflows

• 2+ years in a technical leadership role

• Track record of rapidly adopting and scaling new AI/ML technologies in production environments

• Experience leading teams through the adoption and integration of GenAI/LLM solutions

• Experience with modern development practices (CI/CD, automated testing, code reviews)

• Demonstrated ability to mentor and grow engineers

• Experience working in cross-functional, agile teams

Highly Desirable

• Experience prototyping, evaluating, or deploying GenAI/LLM solutions in real-world settings

• Experience with multi-model orchestration, prompt optimization, and agentic workflow automation

• Experience with cloud-native AI/ML infrastructure and MLOps tooling

• Experience with data pipeline automation, data quality, and governance

• Experience with experiment tracking, analytics, and observability for AI/agentic systems

• Experience thriving in fast-paced, ambiguous environments and balancing rapid delivery with technical excellence

• Experience leading or working with distributed, multidisciplinary teams

Success Metrics

• Model performance (accuracy, precision, recall, latency)

• Agentic workflow reliability and effectiveness

• Turns (iteration cycles, delivery cadence)

• Bugs (defect rate, bug count, resolution time)

• Experiment velocity and time to validated result

• Data quality and insight adoption

• Technical debt reduction and architectural alignment

• Team growth, engagement, and retention

• Stakeholder satisfaction and cross-team collaboration