Technical Architect - Machine Learning

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If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

Role: Technical Architect - Machine Learning

Experience Level: 10+ Years

Work location: Mumbai, Bangalore & Trivandrum

Role & Responsibilities:

• Design and architect multi-layered Agentic AI solutions using Azure AI Foundry and LLM Orchestration frameworks (e.g., LangGraph, Semantic Kernel).

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  Define the technical roadmap for Plan-and-Execute agentic loops, specifically for unstructured data extraction and autonomous system updates.

• Lead model selection and evaluation strategies, comparing high-reasoning models like Claude 3.5 Sonnet with open-weight alternatives (Llama 3.1) for cost and performance optimization.

• Establish enterprise-grade security and governance standards following the NIST AI RMF framework.

• Architect Human-in-the-Loop (HITL) workflows to ensure high-accuracy outputs for mission-critical SAP integrations.

• Design the high-level handoff protocol between agents for multi-step tasks

• Establish the security architecture for agentic access to external vendor portals and third-party APIs using zero-trust principles.

• Provide technical leadership to MLEs and Platform engineers to ensure architectural alignment across workstreams.

Skills expectation:

• Expertise in LLM Orchestration patterns (ReAct, Chain-of-Thought, multi-agent collaboration).

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• Deep experience with Azure AI services, including AI Search, AI Foundry, and Azure OpenAI.

• Strong background in software architecture, including microservices and event-driven patterns (Azure Service Bus)

• Proficiency in Python, advanced prompt engineering and techniques ML frameworks (PyTorch, TensorFlow)

• Experience with AI Governance, compliance, and risk mitigation strategies.

• Proven experience in deploying and monitoring LLMs in a production environment.

• Expertise in tool-calling functions for agents using Playwright or Selenium to navigate and extract data from vendor portals lacking native APIs.

• Familiarity with containerization (Docker, Kubernetes) for ML model serving.