Principal Architect - GenAI

Quantiphi

8d•Remote

About The Position

Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients. Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries. Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms. Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators. We are also proud to be certified as a Great Place to Work—reflecting our commitment to our people and our culture.

Requirements

10+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS.
Hands-on experience on AWS services.
Proven experience using AWS Sagemaker and Bedrock leveraging different types of data sources, Training jobs, real-time and batch applications.
Hands-on experience with Amazon AgentCore for building, deploying, and scaling production-grade agentic AI applications, including agent memory management, tool registry, and observability.
Proficiency in working with LLM APIs (e.g., Claude, Nova, and other third-party LLM providers), including API integration, and multi-model orchestration strategies.
Hands-on experience fine-tuning or optimizing large language models (LLM).
Familiarity with LLM tool use, prompt templating and context management.
Strong expertise in Vector Databases, including indexing strategies, embedding generation, similarity search, and integration with RAG architectures.
Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.
Should have experience with Deep Learning Concepts - Transformers, BERT, Attention models, tokenization, embeddings.

Nice To Haves

Experience with software development, exposure to frontend backend frameworks and communication protocols.
Experience working on Infrastructure as Code (IaC) and CI/CD pipelines.
Experience with NLP concepts: syntactic/semantic analysis, NER etc.

Responsibilities

Designing and developing advanced machine learning models and algorithms to solve complex business problems.
Optimizing and deploying these models on AWS infrastructure, ensuring scalability and reliability.
Design and implement agentic AI architectures using frameworks such as LangChain, Strand Agents etc., enabling autonomous task planning, decision-making, and multi-step reasoning.
Architect and deploy scalable AI solutions on AWS, leveraging services like Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker.
Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.
Develop and maintain Model Context Protocol (MCP) implementations to manage state, context windows, memory, and prompt orchestration across distributed agent systems.
Implementing secure, scalable APIs and integrating with 3rd-party data sources and tools.
Collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.