2 days ago
Responsibilities
- Design and implement generative AI solutions using AWS Bedrock and Amazon AgentCore.
- Define architectures for LLM applications, RAG pipelines, and agentic workflows.
- Develop and orchestrate multi-step agentic workflows with tool usage, task automation, memory, and context management.
- Build and manage embedding, retrieval, and vector database components for RAG systems.
- Integrate LLM capabilities into enterprise applications through APIs and backend services.
- Develop prompt engineering, model evaluation, monitoring, and optimization strategies for quality, latency, and cost.
- Architect and deploy scalable AI solutions using AWS services such as Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker.
- Develop and maintain Model Context Protocol implementations for distributed agent systems.
- Troubleshoot production GenAI systems and establish practices for security, governance, and responsible AI.
- Collaborate with cross-functional teams, provide technical leadership, and mentor team members while remaining hands-on.
Requirements
- At least 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 varied 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.
- Proficiency integrating LLM APIs such as Claude, Nova, and other third-party providers, including multi-model orchestration.
- Experience fine-tuning or optimizing large language models and evaluating zero-shot and few-shot capabilities.
- Strong expertise with vector databases, embeddings, indexing, 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 developers, QA, project managers, and other stakeholders.
- Nice-to-have experience with software development, frontend or backend frameworks, communication protocols, infrastructure as code, CI/CD pipelines, and NLP concepts.
Benefits
- Remote work location in the US.
Tech Stack
Apache AirflowAWS
Categories
About Quantiphi
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Since its inception in 2013, Quantiphi has solved the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve accelerated and quantifiable business results. Learn more at www.quantiphi.com.