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# Technical Architect - ML - GenAI

- **Company:** Quantiphi, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Communications Protocols, Memory Management, Machine Learning, Software Engineering, Systems Integration, Web Applications, Enterprise Software Applications, Large Language Models, Database Optimization, State Machines, Deep Learning, Model Validation, Generative AI, Infrastructure as Code (IaC), Backend, Templating, Kubernetes, Front End Software Development, Api Gateway, Restful APIs
- **Published:** August 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4310ee0226e3ab39

## About the Role

* 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 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.

## Description

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., * 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


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