1 day ago
Responsibilities
- Design and implement enterprise-grade GenAI solutions using AWS Bedrock and Amazon AgentCore.
- Define architectures for LLM applications, RAG pipelines, agentic workflows, and multi-step task automation.
- Build and manage embeddings, retrieval mechanisms, vector databases, APIs, and backend integrations.
- Develop, deploy, monitor, evaluate, and optimize production GenAI systems for quality, latency, scalability, and cost.
- Integrate LLM capabilities with enterprise applications, structured and unstructured data, third-party tools, and data sources.
- Develop and maintain Model Context Protocol implementations for state, context, memory, and prompt orchestration.
- Define security, governance, responsible AI, and engineering best practices.
- Collaborate with application, data, platform, QA, project management, and other stakeholder teams.
- Troubleshoot production issues, provide technical leadership, and mentor team members while contributing hands-on.
Requirements
- 8+ years of relevant hands-on technical experience implementing and developing cloud ML solutions on AWS.
- Hands-on experience with AWS SageMaker and Bedrock, including training jobs and real-time and batch applications using different data sources.
- Experience designing agentic AI architectures with frameworks such as LangChain and Strand Agents.
- Hands-on experience with Amazon AgentCore, including agent memory management, tool registries, deployment, scaling, and observability.
- Experience architecting scalable AI solutions using Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker.
- Proficiency integrating LLM APIs such as Claude and Nova and implementing multi-model orchestration strategies.
- Experience fine-tuning or optimizing large language models and evaluating zero-shot and few-shot capabilities.
- Strong expertise in vector databases, indexing, embedding generation, similarity search, and RAG architectures.
- Experience with at least one workflow orchestration tool, such as Airflow, Step Functions, SageMaker Pipelines, or Kubeflow.
- Experience implementing secure, scalable APIs and integrating third-party data sources and tools.
- Knowledge of deep learning concepts including Transformers, BERT, attention models, tokenization, and embeddings.
- Ability to collaborate with cross-functional teams and stakeholders to understand requirements and implement solutions.
- Preferred experience with software development, frontend and backend frameworks, communication protocols, Infrastructure as Code, CI/CD pipelines, and NLP concepts such as syntactic analysis, semantic analysis, and NER.
Benefits
- Remote work location in the United States.
- Opportunity to work in a culture emphasizing transparency, diversity, integrity, learning, growth, innovation, and professional and personal development.
Tech Stack
Apache AirflowAWS
Categories
About Quantiphi
Quantiphi is an AI-first digital engineering and consulting firm that designs and implements machine learning, data, and cloud solutions for large enterprises across industries. Its teams build production systems—such as generative AI applications, computer vision, and predictive analytics—primarily on Google Cloud and other hyperscalers, delivered as professional services and managed solutions. Founded in 2013 and headquartered in Marlborough, Massachusetts, the company is privately held and a Google Cloud partner.