Teams at Flatworld Solutions

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.