Technical Architect - Machine Learning

Quantiphi is an award-winning, AI-First digital engineering and consulting company focused on delivering high-impact Services and Solutions that help organizations solve what truly matters. We partner with enterprises to reimagine their businesses through intelligent, scalable, and transformative AI driving measurable outcomes at the very core of their operations. Since our founding in 2013, Quantiphi has tackled some of the world’s most complex business challenges by combining deep industry expertise, disciplined cloud and data engineering practices, and cutting-edge applied AI research. Our work is rooted in delivering accelerated, quantifiable business value, not just technology for technology’s sake. Headquartered in Boston, Quantiphi is a global organization with 4,000+ professionals serving clients across key industry verticals, including BFSI, Healthcare & Life Sciences, CPG, MFG, TME etc. As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA, Google Cloud, AWS, and Snowflake, we build and deliver enterprise-grade AI services and solutions that create real-world impact.

We’ve been recognized with:

• 21x Google Cloud Partner of the Year awards in the last 8 years.
• 3x NVIDIA Partner of the Year titles.
• 2x Snowflake Partner of the Year awards.
• We have also garnered top analyst recognitions from Gartner, ISG, and Everest Group.
• We offer first-in-class industry solutions across Healthcare, Financial Services, Consumer Goods, Manufacturing, and more, powered by cutting-edge Generative AI and Agentic AI accelerators.
• We have been certified as a Great Place to Work for the third year in a row- 2021, 2022, 2023.

Be part of a trailblazing team that’s shaping the future of AI, ML, and cloud innovation. Your next big opportunity starts here! For more details, visit: Website or LinkedIn Page.

Role: Technical Architect - ML – Machine Learning (ML)

Responsibilities

• Design and lead the implementation of enterprise-scale AI, Generative AI, RAG, and Agentic AI solutions.
• Lead the evaluation and selection of foundation models, vector databases, embedding models, agent frameworks, retrieval strategies, and supporting technologies based on functional and non-functional requirements.
• Design multi-agent systems, orchestration workflows, tool integration patterns, and evaluation frameworks
• Define and enforce engineering standards within delivery teams covering observability, evaluation, testing, and deployment practices.
• Guide engineering teams through technical design, development, testing, deployment, and
• Conduct architecture reviews, code reviews, and technical mentoring across engineering teams.
• Collaborate with business stakeholders, architects, and delivery teams to translate requirements into scalable technical solutions.
• Contribute to critical implementations, proofs of concept, and architecture validation activities.

Must-Have Skills

• AI Solution Design - Experience designing production-scale AI and GenAI systems, not just implementing them.
• Agent Architecture - Deep understanding of agent orchestration patterns, multi-agent systems, and tool integration approaches.
• Retrieval Architecture - Experience designing RAG, hybrid retrieval, reranking, and retrieval optimization strategies.
• Fine-Tuning - LoRA, PEFT, model customization, and adaptation techniques.
• Reinforcement Learning - Solid understanding of RL concepts and application in agentic Systems.
• Technology Evaluation - Ability to evaluate models, frameworks, vector databases, and AI tooling based on business and technical requirements.
• Engineering Leadership - Drive engineering excellence and technical ownership within delivery teams through rigorous code/design reviews, structured mentoring, and architectural guidance.
• AI Engineering Ecosystem - Strong hands-on expertise across LangGraph, LangChain, MCP, vector databases, and evaluation frameworks.
• Cloud Platforms - Experience designing and deploying AI solutions on GCP and/or Azure.

Good-to-Have Skills

Telecom Solutions - Experience working in telecom industry

Knowledge graph integration Experience designing GraphRAG or hybrid retrieval architectures

combining vector search with graph-based knowledge retrieval.

MLOps / LLMOps Model lifecycle management, observability, and production AI operations.