Engineering Team Lead

Engineering Manager — Full Stack Location : Gurgaon

Function : Engineering

Reports to : CTO

Team size : 7–8 engineers (startup pod)

Why this role

We’re building an enterprise-grade AI-powered recruitment platform with scalable services, polished UIs, and reliable integrations. You’ll lead a small, high-leverage team that ships fast, measures rigorously, and keeps production healthy. A significant part of the work involves applying AI / LLM features where they clearly add value.

What you’ll do

Own delivery end-to-end : roadmap, execution, quality, and timelines for full-stack features and platforms.

• Be hands-on (30–50% coding) : set the technical bar in Java / Python / TypeScript;
• review PRs;

tacklecomplex bugs and design calls.

Architecture & system design : microservices, APIs, eventing, data models, caching, and performance at scale.

Frontend leadership : guide React / Angular patterns (state mgmt, routing, accessibility, perf), design systems, and CI for web.

secure, versioned REST / GraphQL APIs.

tune indexes, migrations, and reliability.

Quality & reliability : testing strategy (unit / integration / e2e), release health, SLIs / SLOs, error budgets, on-call.

cost, scaling, and incident response.

Security & compliance : tenancy isolation, PII handling, secrets, audits—partner with Security as needed.

run effective rituals (standups, planning, postmortems).

translate goals into clear, incremental roadmaps.

track quality, latency,and cost.

What you’ve done (must-haves)

4+ years leading engineers (TL / EM) in high-velocity product teams.

proven system design and API fundamentals.

Shipped and operated production full-stack products at scale (web UI + backend services + DB).

mobile (Flutter) is a plus.

Experience with SQL databases ( Postgres, MariaDB ) : schema design, migrations, performance tuning.

Comfortable with DevOps : GitHub Actions, Docker, basic Kubernetes, IaC (Terraform), and one major cloud (GCP / AWS / Azure).

Observability mindset : logs / metrics / traces, dashboards / alerts, release health, and error budgets.

data-driven decisions.

AI familiarity : LLM basics, prompt patterns, RAG concepts, vector stores, and evaluation at a feature level.

Familiarity with AI SDLC tools (Copilot, Cursor, Claude Code) and test automation.

Nice to have

HRTech / recruitment domain (ATS / CRM, assessments, interview orchestration).

Hands-on with LLM tooling (LangChain / LlamaIndex / DSPy), vector DBs (pgvector / FAISS / Pinecone / Weaviate).

Workflow engines (Temporal / Prefect), event streaming, or multi-tenant SaaS at scale.

Privacy-preserving patterns, regionalization, and policy / guardrail systems.