Technical Architect – ML, GenAI

Quantiphi

GenAI Architect designing scalable AWS Bedrock and AgentCore solutions for Quantiphi, an AI-first digital engineering company. Building LLM, RAG, and agentic AI systems.

Posted 8/4/2026full-timeRemote • 🇺🇸 United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing Generative AI solutions using AWS services, with a strong focus on LLM-based applications, RAG pipelines, and agentic workflows. Proficient in optimizing AI models and integrating LLM capabilities into enterprise applications while ensuring security and governance best practices.

Highest-signal resume keywords

AWS BedrockGenerative AI SolutionsLLM-Based ApplicationsAWS SageMakerAgentic AI Architectures

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Cloud ML SolutionsRAG PipelinesPrompt EngineeringVector DatabasesModel OptimizationAPI IntegrationWorkflow OrchestrationDeep Learning ConceptsFine-Tuning LLMsEmbedding Generation

Soft Skills

Technical LeadershipCollaborationMentoring

Tools & Technologies

Amazon AgentCoreAWS LambdaAWS Step FunctionsAWS S3API GatewayLangChainStrand AgentsAirflowSageMaker PipelinesKubeflow

Industry Keywords

Generative AILLM APIsTask AutomationKnowledge-Driven AISecurity Best PracticesGovernanceModel Context ProtocolNLP Concepts

Tech Stack

Tools & technologies

AirflowAWSCloud

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

8+ years of relevant hands-on technical experience implementing and developing cloud ML solutions on AWS
Hands-on experience with AWS services
Proven experience with AWS SageMaker and Bedrock, including different data sources, training jobs, and real-time and batch applications
Experience designing and implementing agentic AI architectures using frameworks such as LangChain and Strand Agents
Hands-on experience with Amazon AgentCore, including agent memory management, tool registry, and observability
Experience architecting and deploying scalable AI solutions using Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker
Proficiency with LLM APIs such as Claude, Nova, and other third-party providers, including API integration and multi-model orchestration
Hands-on experience fine-tuning or optimizing LLMs
Familiarity with LLM tool use, prompt templating, and context management
Strong expertise in vector databases, indexing strategies, embedding generation, similarity search, and RAG integration
Experience evaluating zero-shot and few-shot LLM capabilities, fine-tuning hyperparameters, task generalization, and model interpretability
Experience developing and maintaining Model Context Protocol implementations
Experience with at least one workflow orchestration tool: Airflow, Step Functions, SageMaker Pipelines, or Kubeflow
Experience implementing secure, scalable APIs and integrating third-party data sources and tools
Ability to collaborate with developers, QA, project managers, and other stakeholders
Experience with deep learning concepts including Transformers, BERT, attention models, tokenization, and embeddings
Nice to have: software development experience and exposure to frontend/backend frameworks and communication protocols
Nice to have: Infrastructure as Code and CI/CD pipeline experience
Nice to have: NLP concepts such as syntactic/semantic analysis and NER

Benefits

Comp & perks

Learning and growth opportunities
Opportunities to interact with colleagues from varied experience and backgrounds around the globe
Diverse and hybrid work culture (company-wide culture statement)