Technical Architect - ML - GenAI

Quantiphi

4d•Remote

About The Position

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: Gen AI Architect (AWS) Experience Level: 8+ Years Work location: Remote (US) Job Overview: We are looking for a Generative AI Architect / Lead to design and deliver enterprise-grade GenAI solutions using AWS Bedrock and Agentcore. This role focuses on building scalable applications leveraging large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI workflows. The ideal candidate will be a hands-on architect who can define solution architecture, guide teams, and actively contribute to development while ensuring performance, scalability, and cost efficiency.

Requirements

8+ 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.
Design and implement agentic AI architectures using frameworks such as LangChain, Strand Agents etc., enabling autonomous task planning, decision-making, and multi-step reasoning.
Hands-on experience with Amazon AgentCore for building, deploying, and scaling production-grade agentic AI applications, including agent memory management, tool registry, and observability.
Architect and deploy scalable AI solutions on AWS, leveraging services like Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker.
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.
Model Evaluation & Optimization: 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.
Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.
Experience implementing secure, scalable APIs and integrating with 3rd-party data sources and tools
Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.
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

Design and implement GenAI solutions using AWS Bedrock and Agentcore
Define architecture for LLM-based applications, including RAG pipelines and agentic workflows
Develop and orchestrate agentic AI workflows, enabling multi-step reasoning, tool usage, and task automation
Build and manage RAG pipelines, including embeddings, retrieval mechanisms, and vector databases
Integrate LLM capabilities into enterprise applications via APIs and backend services
Design and optimize prompt engineering strategies for accuracy, relevance, and performance
Work with structured and unstructured data sources to enable knowledge-driven AI applications
Ensure model evaluation, monitoring, and optimization for latency, cost, and response quality
Collaborate with application, data, and platform teams for end-to-end solution delivery
Define best practices for security, governance, and responsible AI usage
Troubleshoot and resolve issues in production GenAI systems
Provide technical leadership and mentor team members while remaining hands-on