Staff AI Engineer

Accountabilities

• AI system development: Design, develop, and operate production-grade LLM and NLP systems, machine learning services, and data pipelines. Guide critical components from early experimentation and prototyping through deployment, monitoring, and continuous improvement.

• Technical architecture and leadership: Define the architecture, technical standards, and engineering practices for AI systems. Make informed decisions about model selection, build-versus-buy approaches, and trade-offs between accuracy, latency, scalability, and cost.

• AI reliability and evaluation: Build evaluation frameworks, performance metrics, monitoring systems, and traceability mechanisms to measure model quality, detect hallucinations, identify failure patterns, and improve reliability in production.

• Responsible AI implementation: Design appropriate safeguards, explainability mechanisms, and human-in-the-loop workflows to support trustworthy AI outputs, particularly when users rely on the system for sensitive or high-impact decisions.

• Problem discovery and delivery: Collaborate with product teams, customers, and subject-matter experts to translate complex, ambiguous requirements into well-defined AI use cases and deliver practical solutions that generate measurable business value.

• Reusable AI infrastructure: Develop shared evaluation tools, deployment pipelines, data practices, and engineering foundations that make future AI projects faster, safer, more consistent, and easier to maintain.

• Engineering excellence: Lead technical design discussions, conduct architecture and code reviews, document important decisions, and promote high standards for software quality, maintainability, testing, and production readiness.

• Mentorship and team development: Coach engineers across experience levels, share technical expertise, improve development practices, and contribute to hiring, onboarding, and the long-term growth of the AI engineering team.

• Research and experimentation: Stay informed about advances in LLMs and applied machine learning, conduct focused experiments, and translate promising research and emerging technologies into useful production capabilities.

• Cross-functional communication: Work closely with engineering leadership, product stakeholders, and business teams to explain complex technical concepts, communicate trade-offs, and align AI development with organizational priorities.

• End-to-end ownership: Take responsibility for critical AI components throughout their lifecycle, ensuring that solutions remain effective, observable, maintainable, and aligned with evolving business requirements.

Requirements

• Professional experience: At least 8 years of experience building and delivering machine learning systems, with a demonstrated record of technical leadership and ownership of production-grade solutions.

• Programming expertise: Strong proficiency in Python, including the ability to write clean, maintainable, well-tested, production-ready code and support services throughout their lifecycle.

• Machine learning and NLP: Deep, hands-on experience with machine learning, natural language processing, deep learning, and modern large language models, including deploying models in real-world applications.

• Evaluation and monitoring: Experience designing model evaluation frameworks, defining meaningful performance metrics, monitoring deployed models, and implementing processes to identify and address reliability issues.

• Mathematical foundations: Strong knowledge of statistics, probability, and linear algebra, with the ability to apply these concepts to machine learning development, experimentation, and model evaluation.

• Production infrastructure: Experience building and operating scalable machine learning services using Docker and AWS, including serverless computing environments.

• Technical leadership: A proven ability to make architectural and technical decisions that influence broader engineering practices, lead complex projects without relying on formal authority, and balance long-term design with immediate delivery needs.

• Mentorship and collaboration: Experience mentoring engineers, leading design and code reviews, improving team development practices, and fostering a culture of technical excellence.

• Problem-solving: Comfort working with ambiguous, open-ended problems, identifying the most valuable opportunities for AI, and translating complex requirements into clear technical solutions.

• Communication: Excellent written and verbal communication skills, including the ability to explain technical decisions, limitations, and trade-offs to both technical and non-technical audiences.

• Education: A master's degree in Computer Science, Machine Learning, Statistics, Engineering, Mathematics, or a related discipline, or equivalent practical experience.

• Preferred LLM expertise: Experience applying LLMs to non-conversational use cases, including information extraction, classification, structured output generation, and reasoning over complex documents.

• Preferred industry experience: Background in compliance, human resources technology, regulatory environments, or other sensitive and audit-intensive domains where accuracy, traceability, and reliability are essential.

• Responsible AI experience: Familiarity with explainable AI approaches, human-in-the-loop workflows, and mechanisms for improving transparency and trust in AI-powered systems.

• Infrastructure automation: Experience with Terraform or other infrastructure-as-code tools is an advantage.

• Professional mindset: A balance of strategic thinking and hands-on engineering, combined with intellectual curiosity, sound judgment, ownership, adaptability, and a commitment to continuous learning and measurable results.

Benefits

• Competitive compensation: Salary determined through objective market benchmarking, with final compensation based on relevant skills, experience, qualifications, education, and geographic location.

• Additional compensation: Eligibility for an annual performance-based bonus for non-sales roles, subject to applicable compensation policies and individual and company performance.

• Remote-first flexibility: The opportunity to work remotely from India, supporting flexibility and an environment designed to help distributed teams perform at their best.

• Comprehensive healthcare: Medical, dental, and vision insurance options, subject to applicable employee benefit plans.

• Family support: Generous paid parental leave to support employees and their families through important life events.

• Flexible time off: Paid time-off arrangements designed to support personal wellbeing, rest, and work-life balance.

• Financial wellbeing: Access to spending accounts and additional benefits, depending on the applicable employment and benefits plan.

• Long-term rewards: Eligibility for a sabbatical after five years of service, subject to the applicable company policy.

• Professional growth: Opportunities to influence AI architecture, mentor engineering talent, and work on sophisticated machine learning systems with meaningful real-world applications.

• Innovation-focused environment: A culture that encourages experimentation, technical ownership, continuous improvement, and the practical application of emerging AI technologies.

• Global collaboration: The opportunity to work with distributed, cross-functional teams and contribute to technology that supports businesses and professionals across international markets.