Sr. Engineer, Software - GenAI (Hybrid)

Chamberlain Group•Oak Brook, IL

5d•$85,500 - $162,975•Hybrid

About The Position

This role is within Chamberlain Group's (CG) Engineering group. A successful incumbent is expected to contribute to the planning and scoping of agentic AI systems by evaluating models, frameworks, and integration options, and by assisting engineering teams with technical analysis and workflow recommendations. They will also assist in building agentic AI systems using LLMs, RAG, MCP, tool integration, and orchestration frameworks. The role involves conducting analysis and experimentation on agent reasoning, planning, and memory behaviors to guide improvements in contextual interactions and task execution. The engineer will build, test, and optimize pipelines using tools like LangChain, LangGraph, and CAMEL, and collaborate with product, engineering, and research teams to gather requirements and support scalable assistant deployments. Responsibilities include preparing assessments on tooling, infrastructure, and readiness, providing guidance on AI-native engineering patterns and LLM trends, supporting end-to-end delivery by coordinating across teams, and contributing technical analysis to roadmap discussions. The role also involves supporting the refinement of engineering workflows and agile practices, assisting in monitoring delivery metrics and GenAI tool usage, and complying with health and safety guidelines. Maintaining professional knowledge and protecting company information are also key aspects of the role.

Requirements

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
8+ years of experience in software engineering, with at least 1+ years working with LLMs or agentic AI systems.
Strong working knowledge of agentic system concepts, modern AI components, and LLM‑based workflows.
Proficiency in Python and experience supporting production‑grade development.
Hands‑on experience with LLM orchestration tools (e.g., LangChain, LangGraph, LlamaIndex, CAMEL) for analysis and workflow testing.
Understanding of agent reasoning, coordination patterns, memory systems, and RAG pipelines.
Experience with vector databases (FAISS, Pinecone, Weaviate) and embedding‑based retrieval analysis.
Solid grasp of software architecture basics, distributed system concepts, and integration considerations.
Ability to support prototyping efforts and identify early scalability or deployment considerations.
Experience with chatbot frameworks (e.g., LangChain, Rasa) and backend integration workflows.
Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps/model deployment practices.
Strong communication and cross‑team collaboration skills in fast‑paced environments.

Nice To Haves

Master’s degree in Computer Science, Computer Engineering, or related field
Experience in IoT, software services, video, and AI
Experience with consumer electronics, smart home companies will be beneficial
Familiarity with video foundation models, multimodal LLM and/or video summarization use cases.

Responsibilities

Contribute to planning and scoping of agentic AI systems by evaluating models, frameworks, and integration options, and by assisting engineering teams with technical analysis and workflow recommendations.
Assist and build agentic AI systems using LLMs, RAG, MCP, tool integration, and orchestration frameworks.
Conduct analysis and experimentation on agent reasoning, planning, and memory behaviors, providing insights that guide improvements to contextual interactions and task execution.
Build, test, and optimize pipelines using tools such as LangChain, LangGraph, and CAMEL.
Collaborate with product, engineering, and research teams to gather requirements, validate assumptions, and support planning for scalable and performant assistant deployments.
Prepare assessments and recommendations on tooling, infrastructure, observability, and readiness considerations to support Lead‑level decision making.
Provide guidance to teams by sharing insights on AI‑native engineering patterns and emerging LLM ecosystem trends, supporting knowledge transfer across groups.
Support end‑to‑end delivery by coordinating across frontend, backend, and ML teams, monitoring progress, and highlighting risks or quality concerns for resolution.
Contribute technical analysis and inputs to roadmap discussions and sprint planning, ensuring requirements and constraints are surfaced clearly.
Support the development and refinement of engineering workflows, coding standards, and agile practices by providing data, observations, and improvement recommendations.
Assist in monitoring delivery metrics and GenAI tool usage.
Comply with health and safety guidelines and rules.
Protect Chamberlain Group’s reputation by keeping information confidential.
Maintain professional and technical knowledge by attending educational workshops, reading professional publications, establishing personal networks, and participating in professional societies.
Contribute to the team effort by accomplishing related results and participating on projects as needed.