Top Tech Jobs & Startup Jobs

Designs and governs enterprise AI architectures across models, data pipelines, integrations, infrastructure, security, and user interfaces. Supports pre-sales through proposals, technical estimates, cost projections, client presentations, and feasibility validation. Leads delivery design decisions, guides AI/ML and full-stack engineers, defines acceptance criteria, and manages scalability and technical risks. Establishes responsible AI, privacy, governance, and security practices while building reusable reference architectures and mentoring technical teams.

Build and deploy production LLM and machine learning solutions, including RAG pipelines, agentic workflows, document intelligence, classical ML models, evaluation harnesses, guardrails, APIs, and cloud deployments. The role emphasizes measurable quality, cost control, retrieval optimization, MLOps, model selection, and client-facing technical solutioning. Candidates need strong Python, LLM application development, vector database, ML, evaluation, API, Docker, cloud, and Git experience, with 4–7 years in ML or software engineering and at least two years building generative AI applications.

Design, develop, and deploy production-grade agentic AI and multi-agent systems for enterprise workflows. Build tool integrations, MCP servers, memory and retrieval systems, evaluation suites, safety guardrails, human-in-the-loop controls, observability, and reliable containerized services. Integrate with enterprise platforms and APIs while controlling agent cost, latency, and failure modes. Create reusable templates and client-ready demonstrations in collaboration with AI solution leaders.

Build rapid prototypes and production-grade MVPs using JavaScript or TypeScript, React, databases, APIs, containers, and cloud platforms. Own deployment, CI/CD, monitoring, documentation, demo stability, and iterative client-driven enhancements. Collaborate with AI solution leads, designers, and analysts while evaluating third-party tools. Preferred experience includes vector databases, LLM APIs, RAG pipelines, Redis, cloud services, and AI orchestration frameworks.

Leads business analysis across the full IT project lifecycle, including discovery, process modeling, requirements gathering, documentation, Agile delivery support, client demonstrations, training, handover, and post-project enablement. The role creates BRDs, FRDs, use cases, prototypes, scope documents, and traceability matrices; coordinates with technical teams; manages requirement changes; supports pre-sales activities; and presents solutions to technical and non-technical stakeholders.

Leads enterprise AI solutioning from discovery and architecture through product roadmapping, delivery, client enablement, and knowledge transfer. Owns reusable AI platform capabilities, translates ambiguous requirements into scalable productized solutions, leads cross-functional squads, manages executive client communications, supports presales and commercial activities, and mentors solution consultants. Evaluates LLM, generative AI, agentic AI, RAG, NLP, analytics, and automation opportunities while developing solution accelerators and reference architectures.