About the role
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!
Originally posted on Himalayas