Agentic AI and Data Engineer
About this role
Job Description
Remote Work:
No
Job Number:
R0234847
Location:
Honolulu,HI,US
Agentic AI and Data Engineer
The Opportunity:
As an experienced engineer, you know how to design, develop, and deliver production-grade agentic AI systems that demonstrate the practical value of generative AI, large language models ( LLMs ) , and autonomous workflows. This role combines deep technical expertise with strong product skills to design AI applications that leverage prompting, retrieval-augmented generation ( RAG ) , agentic orchestration, evaluation pipelines, and human-in-the-loop systems to deliver measurable impact.
You will architect modular, reusable AI application patterns, integrate multiple model providers such as cloud-hosted, local, and hybrid , and apply modern GenAI stack capabilities, including structured prompting, tool use, workflow orchestration, and multi-modal reasoning. You will design solutions deployable across various contexts, from cloud-hosted platforms to portable, self-contained builds, optimizing for latency, cost efficiency, observability, and safety. You will rapidly prototype and iterate using AI-assisted development tools, validating hypotheses through eval-driven development and continuous experimentation.
In this role, you’ll define the direction of mission-critical agentic systems by selecting and combining prompting strategies, RAG architectures, agentic workflows, and fine-tuned or foundation models as appropriate. You’ll be part of a large community of AI and ML engineers across the company, collaborating with data engineers, data scientists, solutions architects, and product owners to deliver world-class solutions.
What You’ll Do:
• Design adaptable agentic AI architectures that support multiple model providers, tool ecosystems, modalities, and deployment modes. • Build modular and reusable components for prompting, retrieval, orchestration, tool execution, memory management, and evaluation to enable rapid development of new AI capabilities. • Integrate LLMs, embeddings, RAG pipelines, structured outputs, and long-context or memory mechanisms into production-ready systems. • Apply advanced prompting techniques such as few-shot, chain-of-thought, tool-calling, and function-calling, orchestration frameworks such as LangChain or equivalent , and agentic architectures such as MCP, A2A, or similar patterns, to enable goal-directed autonomy with guardrails, observability, and human oversight, including planning, tool use, delegation, and recovery from failure. • Design and implement evaluation frameworks, both offline and online, to measure correctness, robustness, safety, and business impact of AI systems. • Optimize models and workflows for cost, latency, reliability, and scalability, using systematic benchmarking and experimentation. • Develop data pipelines for ingestion, cleaning, chunking, embedding, indexing, and continuous refresh of structured and unstructured data for RAG and memory systems. • Combine text, audio, vision, and other modalities in unified processing workflows, including document understanding, transcription, summarization, and cross-modal reasoning. • Leverage vector databases, hybrid search, reranking, and retrieval optimization techniques to enhance grounding and reduce hallucination in RAG systems. • Incorporate guardrails, safety filters, access controls, and monitoring mechanisms to ensure responsible and secure deployment of agentic AI systems. • Deploy AI services securely and at scale on AWS or equivalent cloud platforms. • Use containerizing, including in Docker or Kubernetes, or serverless approaches for flexible deployment. • Apply CI / CD and eval-driven development best practices for AI systems, including automated testing of prompts and workflows, versioning of prompts and agents, and safe rollout of model updates. • Use asynchronous programming and event-driven patterns to support scalable, long-running, or multi-agent workflows. • Leverage modern build and packaging workflows to deliver optimized, portable application artifacts. • Use AI assistance tools to accelerate development, debugging, and system design while maintaining engineering rigor and code quality. • Collaborate with clients to identify high-value AI opportunities and define solution requirements. • Present AI capabilities and technical solutions to both technical and non-technical stakeholders. • Lead workshops and prototyping sessions to accelerate adoption. • Provide guidance on responsible AI practices, ethics, and compliance.