Top Tech Jobs & Startup Jobs

Lead end-to-end client AI transformation engagements: discover and frame executive problems, design and build production-credible PoAs (data pipelines, integrations, LLM/RAG apps) within 2–3 weeks, present to executives, hand off to delivery, mentor junior FDEs, and convert PoAs into full engagements.

Design, develop, and deliver scalable software systems and APIs across the full lifecycle. Lead architecture, integrate AI/ML components, implement microservices/serverless and CI/CD, ensure security, observability, and performance, mentor engineers, and collaborate with product and business stakeholders to meet timelines and quality standards.

Develop and operate production AI/ML systems, including distributed model infrastructure, automated evaluation and testing, GPU optimization, secure CI/CD, and performance analytics. The role owns features end to end from architecture through production, performs model benchmarking and error analysis, and manages reliable deployment across multi-node clusters.

Build enterprise applications and polished user interfaces for agentic AI experiences. Own full-stack features from frontend architecture through backend integration, testing, deployment, and production support. Develop React and TypeScript interfaces for multi-step agent workflows, tool calling, approvals, retrieval, state management, errors, and human-in-the-loop interactions. Integrate APIs, agent orchestration layers, and enterprise systems while contributing to backend development, architecture reviews, reliability, and production engineering.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Lead technical vision and architecture for a large software platform. Drive system design, platform capabilities (APIs, event-driven systems, data pipelines), cloud and DevOps practices, and AI/LLM integration. Solve cross-team technical problems, set engineering standards, mentor senior engineers, and align engineering with product and business strategy while ensuring reliability, security, and cost optimization.

Plan and drive cross-functional programs from design through launch. Coordinate teams, milestones, dependencies, risks, and deliverables while monitoring metrics and dashboards. Partner with product, engineering, marketing, operations, and go-to-market stakeholders to align priorities, resolve gaps, and maintain execution. Present clear progress updates, risks, trade-offs, and recommendations to senior leaders, adapting plans and improving processes to deliver measurable customer and business impact.

Own the roadmap and lifecycle of AI-powered products, from problem definition through launch and iteration. Partner with engineering and data science to assess feasibility, define requirements and metrics, run experiments, and balance model quality, cost, latency, and user experience. The role requires hands-on AI prototyping or workflow automation, strong communication across technical and business teams, and accountability for product outcomes.

Build and maintain Java backend services, REST APIs, and full-stack web application features. Develop frontend functionality, use Python for supporting work, and apply AI coding tools such as Claude Code for development, testing, and debugging. Collaborate on design decisions and code reviews, troubleshoot issues, and support application deployment and maintenance across cloud environments.

Leads the design, evaluation, deployment, and monitoring of production AI systems across generative AI, agentic AI, computer vision, forecasting, and optimization. Establishes rigorous metrics for accuracy, uncertainty, bias, hallucinations, drift, explainability, and failure modes; conducts experiments and communicates business impact, risks, and trade-offs. Builds solutions using foundation models, RAG, agentic workflows, unstructured data, and MLOps practices.