Staff AI Engineer in भारत

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Staff AI Engineer based in India (Remote-First). 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. 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.