Top Tech Jobs & Startup Jobs

Design and build end-to-end AI-native applications: React/TypeScript frontends, scalable backend microservices, LLM/agent orchestration, data/search systems, and platform infrastructure. Lead architecture, mentor engineers, and champion DevOps/AIOps, observability, security, and production-grade AI deployments.

As a Staff Platform Engineer, you will lead the architecture and automation of cloud-native platforms, enhance operational reliability, and mentor engineers while leveraging Azure and DevOps methodologies.

Lead large-scale programs critical to business outcomes, managing execution, risks, and stakeholder alignment. Drive operational excellence across engineering initiatives.

The role involves generating qualified leads, crafting proposals, and supporting business development for a pioneering AI company targeting Fortune 1000 clients.

Lead the design and development of an innovative enterprise AI platform, mentoring junior engineers and ensuring operational excellence.

Contract SME to design end-to-end commercial lending workflows, produce reusable artifacts (workflow maps, persona maps, document/data matrices, credit-package models, policy/approval logic, playbooks, validation rubrics), assess product readiness, and advise on where AI can safely support lending and servicing processes.

Design and architect solutions for enterprise clients in financial services. Lead implementations, provide technical expertise, and mentor junior engineers.

As a Senior AGI Engineer, you will develop AGI solutions, integrate features in client environments, and collaborate across teams to deliver measurable results.

Lead and grow high-performing engineering teams to design, architect, and deliver scalable, production-grade AI-driven enterprise platforms. Own technical direction, system reliability, performance, and project delivery using cloud-native architectures and MLOps. Collaborate cross-functionally with research, product, and design, recruit talent, and establish engineering best practices to enable enterprise adoption of explainable, auditable AI systems.

Lead end-to-end engineering for an AI-native enterprise platform: set architecture and platform strategy, harden AI quality and reliability, scale delivery and operations, convert deployment patterns into reusable platform capabilities, and build a distributed US-India engineering organization with strong cross-functional alignment and enterprise-grade governance.