Resource 1, Inc.
Sr. AI Engineer - Contract - Remote - 6 months+
Resource 1, Inc., Naperville, Illinois, United States, 60564
Resource 1 is seeking a Senior AI Engineer for a long-term, remote contract with our client in the Healthcare industry. Initial contract duration is 6 months, with expected extensions. This can be done 100% remotely from anywhere in the US. Selected individual will be brought in to help develop and deliver next-generation AI solutions across the healthcare enterprise . This role is hands-on and ideal for an engineer experienced in building GenAI and multi-agent systems using modern AI frameworks and Google Cloud Platform (GCP). Will collaborate closely with other engineers to design, build, test, and optimize AI capabilities within a scalable production environment.
Key Responsibilities
Develop and enhance enterprise-scale multi-agent systems leveraging LLMs and autonomous agent frameworks, using tools such as Google ADK, Agentspace, MCP, RAG, and A2A orchestration.
Contribute to the design and implementation of RAG pipelines using BigQuery and Vertex AI for knowledge grounding and factual response accuracy.
Implement and tune agent reasoning workflows including orchestration, grounding, decision-making, and multi-step reasoning.
Build and support distributed training workflows, online inference systems, and low-latency serving architectures leveraging Google Cloud services
Develop secure and scalable AI components including reusable orchestration layers, connectors, and observability hooks.
Participate in developing agent governance and compliance frameworks aligned with enterprise standards.
Translate business features and requirements into technical implementation tasks and contribute to solution design discussions.
Support deployment pipelines, operational monitoring, troubleshooting, and optimization of production AI systems.
Required Qualifications
Degree in Computer Science, AI / ML, or related technical field.
Hands-on experience in Generative AI and agentic AI development.
4–5 years of total experience in AI / ML engineering or applied machine learning.
Experience building and deploying production AI / ML systems.
Solid understanding of modern model architectures including transformers, embeddings, and prompt engineering concepts.
Hands-on expertise with Vertex AI (training, pipelines, deployment, orchestration, and monitoring) and Google Cloud native AI services.
Experience with one or more agent frameworks (i.e. Google ADK / Agentspace, LangChain / LangGraph, LlamaIndex, CrewAI or AutoGen)
Python and LLM integration , including MCP and A2A orchestration.
Experience with Kubernetes, Cloud Run, Dataflow or Pub / Sub
Preferred Qualifications
Experience with AI observability, responsible AI frameworks, and model monitoring tools (Vertex AI Monitoring, BigQuery logging, Looker dashboards).
Experience with multi-modal models and / or advanced optimization strategies.
Contributions to open-source AI tooling or published applied work.
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Key Responsibilities
Develop and enhance enterprise-scale multi-agent systems leveraging LLMs and autonomous agent frameworks, using tools such as Google ADK, Agentspace, MCP, RAG, and A2A orchestration.
Contribute to the design and implementation of RAG pipelines using BigQuery and Vertex AI for knowledge grounding and factual response accuracy.
Implement and tune agent reasoning workflows including orchestration, grounding, decision-making, and multi-step reasoning.
Build and support distributed training workflows, online inference systems, and low-latency serving architectures leveraging Google Cloud services
Develop secure and scalable AI components including reusable orchestration layers, connectors, and observability hooks.
Participate in developing agent governance and compliance frameworks aligned with enterprise standards.
Translate business features and requirements into technical implementation tasks and contribute to solution design discussions.
Support deployment pipelines, operational monitoring, troubleshooting, and optimization of production AI systems.
Required Qualifications
Degree in Computer Science, AI / ML, or related technical field.
Hands-on experience in Generative AI and agentic AI development.
4–5 years of total experience in AI / ML engineering or applied machine learning.
Experience building and deploying production AI / ML systems.
Solid understanding of modern model architectures including transformers, embeddings, and prompt engineering concepts.
Hands-on expertise with Vertex AI (training, pipelines, deployment, orchestration, and monitoring) and Google Cloud native AI services.
Experience with one or more agent frameworks (i.e. Google ADK / Agentspace, LangChain / LangGraph, LlamaIndex, CrewAI or AutoGen)
Python and LLM integration , including MCP and A2A orchestration.
Experience with Kubernetes, Cloud Run, Dataflow or Pub / Sub
Preferred Qualifications
Experience with AI observability, responsible AI frameworks, and model monitoring tools (Vertex AI Monitoring, BigQuery logging, Looker dashboards).
Experience with multi-modal models and / or advanced optimization strategies.
Contributions to open-source AI tooling or published applied work.
#J-18808-Ljbffr