Machine Learning Engineer

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


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: Machine Learning Engineer

Experience Level: 2+ Years 

Work location: Mumbai, Bangalore & Trivandrum 

Notice Period: 0- 15 days

Role & Responsibilities: 

• Develop and optimize "Agentic" workflows for complex business processes like Buyer Automation and Hour Meter extraction.

• Implement advanced RAG (Retrieval-Augmented Generation) and tool-calling capabilities to enable agents to interact with external APIs and databases.

• Experience with high-reasoning models like Claude 3.5 Sonnet,  with open-weight alternatives (Llama 3.1) for cost and performance optimization.

• Develop robust benchmarking and evaluation pipelines using frameworks like LangSmith or RAGAS to track extraction accuracy.

• Fine-tune and prompt-engineer models to handle specific industrial data formats and unstructured documents.

•  Collaborate with Data Engineers to build seamless data pipelines that feed into agentic reasoning modules.

•  Optimize model latency and token consumption to ensure scalable and cost-effective deployments.

Skills expectation: 

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

• 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.

• Expert-level knowledge of LangChain, LangGraph, or Haystack.

• Hands-on experience with vector databases and semantic search optimization.

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

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

• Develop the logic for the Streamlit-based review application to present agent outputs to business users.

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