Fractal Analytics

Design and guide enterprise AI architecture across Generative AI, Agentic AI, RAG, and AI-enabled business solutions. This hands-on architect will create production-ready patterns using Snowflake Cortex, AWS AI services, and modern LLM frameworks while advising executive stakeholders.

Full Stack Senior Engineer building end-to-end data-to-decision systems for Fortune 500 clients using React and Python. The role combines frontend and backend development, data science integration, visualization, troubleshooting, and high-traffic application delivery.

Drive enterprise AI adoption by training business users and building practical Claude Cowork workflows that address real operational needs. This hands-on consultant role combines stakeholder enablement, prompt engineering, workflow development, and adoption scaling.

Lead the enterprise architecture of Azure-based agentic AI solutions, defining deployment patterns, orchestration, integrations, security, observability, and production-readiness. The role partners with client stakeholders and engineering teams to turn procurement workflows into scalable, governed AI solutions.

Own the technical success of Cogentiq I2C deployments for strategic finance accounts, turning customer problems into production AI systems. This senior, customer-facing engineering role combines architecture, hands-on coding, deployment leadership, incident response, and advisory work.

Lead the design and development of Python-based automation solutions, Power Platform workflows, and AI/LLM-enabled applications for the Operational Excellence team in Mumbai. The role focuses on RAG pipelines, agentic workflows, integrations, and scalable backend services.

Build the web application and backend services for Cogentiq I2C, an agentic AI platform automating enterprise order-to-cash workflows. You will own full-stack features, reliable agent orchestration, ERP integrations, and production support across Azure and GCP.

Senior MLOps Engineer responsible for automating the end-to-end machine learning lifecycle on Databricks, including CI/CD, model deployment, orchestration, and reproducible experimentation. The role also enables LLM applications through embeddings, RAG architectures, vector databases, and LangChain workflows.