• Contribute to the design, implementation, and maintenance of the LLM Gateway, focusing on features like unified API endpoints for internal/externally hosted LLM, rate/token limit management, and intelligent failover mechanisms to boost uptime and reliability. • Designed and developed ML and Generative AI systems in cloud-based production environments at scale. • Build and manage enterprise-grade RAG applications using embeddings, vector search, and retrieval pipelines. • Implement and operationalize agentic AI workflows with tool use using frameworks such as LangChain and LangGraph. • Drive adoption of MLOps / LLMOps practices, including CI/CD automation, versioning, testing, and lifecycle management. • Establish best practices for observability, monitoring, evaluation, and governance of GenAI pipelines in production. • Strong ownership mindset and platform thinking. • Ability to lead AI platform delivery from concept to production.

• Spend the majority of your time embedded with engineering teams — working alongside developers to stand up AI coding tools, establish platform standards, and introduce agentic workflows into their daily delivery process • Run hands-on enablement programs including workshops, lunch-and-learns, one-on-one coaching sessions, and team-level onboarding for AI development tools like Claude Code and GitHub Copilot • Assess team-level AI readiness across the 350+ person technology organization — identifying gaps in documentation, coding standards, context files, and workflow design that limit AI effectiveness • Prescribe and deliver targeted interventions that help teams get more value from the agentic platform — from CLAUDE.md setup and prompt engineering to agentic workflow configuration and best practice adoption • Collect structured feedback from every team you serve and translate it into actionable requirements and adoption signal for the platform team’s backlog • Produce playbooks, best practice guides, and enablement materials that help engineering teams integrate AI tools into their existing Git, Jira, Confluence, and GitHub Actions workflows without disrupting delivery • Define and track adoption indicators across teams — measuring what’s working, what’s stuck, and what needs adjustment — and present clear signal to platform leadership on enablement progress • Serve as the connective tissue between the platform team’s capabilities and the engineering organization that depends on them — equal parts technologist, coach, and internal consultant

• Build and further develop a scalable lead generation strategy for our L&D business • Use AI tools and automation to identify and engage new customers • Identify tenders and business opportunities in the corporate learning environment • Develop and test data-driven outreach and prospecting strategies • Work closely with Sales, Marketing and our international parent company NIIT to drive joint go-to-market initiatives • Design and organize events, roundtables and networking formats for lead generation • Support in pre-sales, e.g., preparing customer meetings and proposals • Build and continuously improve a structured lead and opportunity funnel

Role Description As a Senior Full-Stack AI Engineer, you will play a critical hands-on role in building and evolving Superbench's AI-powered platform. Working closely with product and leadership, you will design, develop, and ship intelligent, customer-facing features that leverage modern AI capabilities. - Design and build AI-powered product features, including conversational interfaces, RAG pipelines, and multi-step / multi-agent workflows. - Own the implementation of backend and frontend systems across the Superbench platform. - Translate product requirements into scalable technical solutions, particularly for AI-driven use cases. - Work hands-on across the stack: Node.js/TypeScript and Python backend services, React frontend applications. - Integrate LLMs and AI tooling into production systems, ensuring reliability, performance, and strong user experience. - Build and maintain APIs, services, and data pipelines that support AI functionality. - Collaborate closely with product and design to iterate quickly on AI-driven features. - Contribute to engineering best practices around testing, code quality, and system reliability. - Participate in code reviews and support knowledge sharing across the team. Qualifications - 6+ years of professional software engineering experience, with a strong focus on building production systems. - 4+ years of backend engineering experience, primarily using Node.js frameworks (e.g. Express, NestJS) and modern TypeScript. - 2+ years of frontend engineering experience building user-facing applications with React. - 2+ years of practical experience building and integrating AI systems into production applications. - Deep understanding of JavaScript and TypeScript. - Strong experience designing and building scalable backend systems, APIs, and services. - Solid experience with relational databases (e.g. PostgreSQL, MySQL), including schema design and query optimization. - Hands-on experience with NoSQL databases (e.g. MongoDB). - Hands-on Python experience (2+ years), particularly for AI workflows, data processing, or backend services. - Strong experience building AI-powered customer-facing features, including: - Retrieval-Augmented Generation (RAG) pipelines. - Multi-step and/or multi-agent AI workflows. - Prompt design, evaluation, and iteration. - Tool-using agents and orchestration frameworks. - Experience working with AI frameworks and tools such as OpenAI SDK, LangGraph, MCP, or similar. - Hands-on experience with vector databases (e.g. Pinecone or equivalents). - Proven experience integrating complex AI flows into real user-facing products. - Strong problem-solving skills and ability to work in ambiguous environments. - Strong communication skills, with the ability to explain technical concepts clearly. - Strong spoken and written English. Requirements - Experience working in early-stage startups or fast-paced product environments (nice to have). - Familiarity with cloud platforms (e.g. GCP), CI/CD pipelines, and basic DevOps practices (nice to have). - Experience with event-driven architectures, background jobs, or message queues (nice to have). - Experience building real-time or conversational systems (e.g. chat, messaging, workflow automation) (nice to have). - Exposure to analytics, data pipelines, or reporting systems (nice to have). - Experience working with multi-tenant SaaS platforms (nice to have). - Familiarity with security best practices, authentication/authorization, and data privacy considerations (nice to have). - Experience collaborating in remote or distributed teams (nice to have). Benefits - Fully remote role with a distributed team. - Flexible working hours - we care about outcomes, not clock-watching. - Autonomy to structure your day, with clear communication and accountability. - A true leadership role in an early-stage startup. - Ownership over core technical decisions during a critical company pivot. - Direct influence on product direction, architecture, and long-term technical strategy. - Competitive compensation, commensurate with experience and seniority. - MacBook Pro provided. - Unlimited PTO (after a 3-month probationary period). - Grow into a long-term technical leader as the company scales. - Deepen your expertise in AI-first product development, including conversational AI, RAG, and agentic systems. - Work closely with founders and leadership. - Freedom to experiment, learn, and introduce better tools, processes, and practices.