Staff AI Engineer

IndiaRemoteFull-timelead

Machine Learning EngineerData

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Quick Summary

Requirements Summary

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

Technical Tools

Machine Learning EngineerData

As a Staff AI Engineer, you will shape the technical direction and development of advanced AI systems that solve complex, high-stakes business challenges. You will design, build, and deploy production-ready large language model (LLM) and natural language processing (NLP) solutions, ensuring they remain reliable, accurate, and scalable. Working at the intersection of applied machine learning, software engineering, and AI innovation, you will turn ambiguous business needs into practical, impactful products. You will establish robust evaluation frameworks, monitoring systems, and safeguards that promote trustworthy AI and responsible human oversight. Alongside hands-on development, you will mentor engineers, influence architectural decisions, and establish reusable foundations that accelerate delivery across teams. This remote-first opportunity is ideal for a technically accomplished AI leader who enjoys combining strategic thinking with coding, experimentation, and cross-functional collaboration.

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

~2 min read

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.

What We Offer

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✓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.