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Open nowPosted 11 days ago

Technical Architect - ML - GenAI

Quantiphi76 open roles

Where
USA - Remote
Work mode
Remote
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Your applicationOpen nowTechnical Architect - ML - GenAIQuantiphi · USA - Remote

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The clock on this job

Early applications get read.

13.1% of postings close within 7 days. Measured by our own scanner across the market. Quantiphi postings stay open a median of 49 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.1%7 days
  4. 21.1%14 days
  5. 35.7%30 days
  6. 58.7%60 days
This job: posted 11 days ago

Quantiphi median: 49 days open

The posting

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 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.

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

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