About Leena AI

Leena AI is an AI work assistant designed for modern enterprises. Equipped with Leena AI’s proprietary large language model, WorkLM, Leena AI empowers enterprises worldwide to redefine how employees engage with work, delivering a transformative impact on productivity and efficiency.

WorkLM, built on Leena AI's breakthrough language model architecture, possesses an unparalleled predictive text generation capability, producing human-like responses in context. This empowers employees with a versatile toolset to accomplish tasks with exceptional precision and speed, unlocking unprecedented levels of productivity and efficiency.

Leena AI seamlessly integrates with 1000+ applications, including SAP, Salesforce, ServiceNow, ADP, Oracle, Workday, and Microsoft Office 365. The company’s solutions have been successfully deployed across 90+ countries, serving over 400+ customers, including leading enterprises such as Nestlé, Puma, Coca-Cola, Sony, and Etihad Airways. Founded in 2018, Leena AI supports 100+ languages globally, and millions of employees use the work assistant daily.

For more information, visit https://leena.ai.

Leena Ai

About Leena.ai Leena.ai is an enterprise agentic AI platform automating work across HR, Finance, and Legal functions for global&nbsp;enterprises. We compete — and win — against ServiceNow+Moveworks, Workday Illuminate, and vertical point&nbsp;solutions by offering vendor-agnostic cross-functional orchestration, the broadest connector coverage in the&nbsp;category, and deployment timelines measured in weeks rather than quarters. We are a Series B+ company&nbsp;headquartered in New York, with our largest engineering hub in Gurgaon. The Role&nbsp;We are hiring a hands-on Tech Lead to own a pod of 4–6 engineers within our Knowledge Management / NLP&nbsp;org, or across adjacent agentic AI platform pods depending on fit. You will design and ship production systems&nbsp;that power enterprise knowledge retrieval, grounding, and agentic reasoning across the Leena.ai platform — the&nbsp;engine behind every customer-facing agent. This is an IC-heavy leadership role. Expect to spend roughly 60–70% of your time in code, architecture, and&nbsp;design reviews, and 30–40% on mentoring, planning, and cross-functional alignment. 
What You’ll Own • Knowledge &amp; agent architecture: Architect and ship production RAG, knowledge ingestion, and agentic&nbsp;reasoning pipelines that operate across enterprise systems of record (Workday, ServiceNow, SAP,&nbsp;Confluence, SharePoint, and many more). • Evaluation &amp; quality: Build and maintain custom golden datasets and eval harnesses; distinguish&nbsp;retrieval failures from generation failures; set and enforce production accuracy, latency, and cost SLOs. • Multi-LLM orchestration: Work across Anthropic, OpenAI, and Gemini model families. Own model&nbsp;selection tradeoffs (capability vs. cost vs. latency), prompt iteration, function-calling/tool-use patterns,and fallback strategies. • Technical leadership: Set the engineering bar for the pod — design docs, code reviews, on-call discipline, incident response, and production quality gates. • Platform leverage: Partner with Product and Implementation on reference architectures that compress&nbsp;time-to-value for new enterprise deployments. • People growth: Mentor 4–6 engineers; grow at least one to senior-level scope within the year. Must-Haves • 5+ years of software engineering experience with a track record of shipping production software. • Strong production engineering in Python and Node.js. Comfort reading and contributing to a React&nbsp;frontend when the work crosses the boundary. • Hands-on experience building LLM-powered production systems with at least one major provider (Anthropic, OpenAI, or Gemini): RAG pipelines, function-calling / tool-use, prompt and eval iteration&nbsp;loops. • Depth in one or more of: information retrieval, NLP, knowledge graphs, semantic search, vector databases, or chunking / embedding strategies. • Production ownership on AWS — observability, cost discipline, and incident response, not just familiarity. • Demonstrated ability to instrument evals that separate retrieval failures from generation failures, and to&nbsp;drive accuracy improvements off that signal. • Track record of shipping customer-facing features in enterprise SaaS environments. Strong Pluses • Experience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen) or having built&nbsp;equivalent orchestration in-house. • Hands-on with evaluation tooling: RAGAS, DeepEval, LangSmith, Arize Phoenix; familiarity with&nbsp;benchmarks like CRAG. • Experience working across multiple LLM providers in production (routing, fallback, cost/latency-aware&nbsp;model selection). • Enterprise security and compliance exposure: SOC 2, ISO 27001, GDPR, data residency, PII handling. • Experience with enterprise integrations: ITSM, HRIS, ERP, or collaboration systems (Workday, ServiceNow, SAP SuccessFactors, Oracle, Confluence, SharePoint). • Prior Series B–D startup experience and comfort operating with ambiguity. 
How You Will Be Measured (First 6 Months) • Pod velocity and quality: on-time delivery of two quarterly roadmap themes with defensible evaluation&nbsp;results. • Production reliability: meeting SLOs for accuracy, latency, availability, and cost per resolved query. • Platform leverage: reusable components, primitives, or patterns your pod contributes that other teams&nbsp;adopt. • Team growth: measurable progression of at least one engineer on your pod; healthy retention and&nbsp;engagement. Why Leena.ai • Work on agentic AI deployed in production at global enterprises — not demos, not pilots. • Technical depth: cross-functional orchestration across HR, Finance, and Legal at enterprise scale is a non-trivial engineering problem, and knowledge/NLP is at the core of it. • Direct line to leadership: you will work closely with the SVP of Engineering and Product leadership. • Competitive compensation, meaningful equity, and the trajectory of a Series B+ category leader.