Top Tech Jobs & Startup Jobs in Mumbai

Develop, customize, integrate, test, deploy, and support Dynamics 365 Business Central solutions using AL and extensions. Collaborate with cross-functional teams to translate business requirements into technical designs, integrate third-party systems through APIs and web services, document solutions, maintain coding standards, support upgrades, tune performance, and diagnose operational issues. The role also involves Power Platform capabilities, reporting, database work, CI/CD tools, and specialized Business Central workflow and finance integrations.

Own the end-to-end architecture of a scalable B2B procurement marketplace integrating React, .NET, Azure, Entra ID, SAP, WMS, TMS, and other enterprise systems. Design APIs, middleware, data flows, security, and scalability patterns; lead technical discovery, maintain architecture documentation, support implementation alignment, and present technical approaches to clients and stakeholders.

Own and prioritize delivery backlogs for a connected consumer grooming ecosystem spanning mobile apps, IoT devices, firmware, BLE, hardware, UX, and cloud services. Translate approved Epics into user stories, acceptance criteria, and sprint-ready work. Coordinate dependencies, compliance, releases, risks, documentation, and distributed engineering teams while serving as the primary delivery liaison between Product Management and technical partners.

Lead a 10–15-person engineering team building agentic AI systems from the ground up. Set architecture strategy for planning, memory, tool use, multi-agent coordination, guardrails, observability, evaluations, reliability, and safety. Drive adoption of an AI-Driven Development Lifecycle, partner with product managers on ambiguous requirements, oversee delivery and technical reviews, and support hiring, mentorship, career growth, and leadership reporting.

Owns requirements elicitation, documentation, validation, and traceability for a B2B procurement ordering portal. Facilitates stakeholder workshops, produces functional specifications and user stories, performs gap analysis, supports UAT, and bridges business needs with engineering delivery. The role involves complex integrations with SAP, WMS, logistics, finance, and enterprise systems in a scope-controlled environment.

Lead the architecture, development, and deployment of enterprise AI systems using LLMs, RAG, multimodal models, and multi-agent architectures. Drive prototyping through production delivery, establish LLMOps practices, and ensure model reliability, security, and governance. Serve as a technical advisor during presales, collaborate with product and data teams, and mentor junior engineers.

Gather and document business and functional requirements for card and payment solutions. Collaborate with stakeholders, product owners, engineering, QA, and processors across the SDLC. Define user stories, process flows, acceptance criteria, and technical specifications; analyze impacts and dependencies; support UAT, integration testing, defect triage, releases, and production issues. Apply expertise in card processing, payment networks, ISO 8583, tokenization, digital wallets, chargebacks, reconciliation, and regulatory compliance.

Build production AI/ML systems for extracting structured data from complex documents. Responsibilities include document ingestion, OCR, confidence modeling, RAG knowledge bases, agentic workflows, human-in-the-loop review, reprocessing and diffing, model optimization, evaluation, monitoring, and governance. The role also provides technical mentorship to mid-level AI engineers and owns systems end to end.

Designs and deploys production-grade AI agents, RAG systems, multi-agent workflows, SDKs, and scalable LLM applications. Builds secure knowledge architectures, integrations, MLOps and LLMOps pipelines, governance controls, and evaluation frameworks. Serves as a technical authority in client engagements, mentors engineers, supports presales and recruitment, and drives engineering standards across AI projects.

Build end-to-end AI agent applications, including React and TypeScript interfaces, backend APIs, enterprise integrations, workflow orchestration, approval flows, dashboards, and human-in-the-loop features. Integrate LLM services, vector stores, databases, and observability tools while implementing secure authentication, RBAC, tenant-aware access, testing, CI/CD, logging, and deployment patterns.