Technical Architect - ML - GenAI

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.

• Key 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

• Must have:

-

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 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 have:

-

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.  
 **

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!