As a Forward Deployed Engineer - AI within IBM Consulting, you will partner directly with clients to transform complex business challenges into production-ready AI solutions. You will build and scale end-to-end GenAI, RAG, and agentic AI applications, focusing on LLM-powered agents, tool use, orchestration, and prompt engineering. Your daily work involves developing multi-step workflows, preparing structured and unstructured data, and integrating AI with enterprise systems and APIs. You will utilize a technical stack including Python, SQL, LangChain, LangGraph, CrewAI, and PyTorch, while deploying on platforms like AWS Bedrock, Azure AI Foundry, or Google Vertex AI. Key responsibilities include implementing MLOps/LLMOps, establishing evaluation frameworks, and ensuring responsible AI practices to deliver reliable, secure, and measurable business impact for diverse industrial clients.
What does a Forward Deployed Engineer earn?
Median $208800 from 49 postings across 21 companies.
Identify business opportunities and define AI use cases with client teams.
Develop end-to-end GenAI, RAG, and agentic AI applications using LangGraph and CrewAI.
Design LLM-powered agents including prompt engineering, tool orchestration, and evaluation frameworks.
Prepare structured and unstructured data and integrate AI solutions with enterprise APIs.
Apply MLOps and LLMOps practices to ensure solutions are secure and production-ready.
Present insights and outcomes to clients to support adoption and create reusable assets.
What we're looking for
Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
Master's or PhD in a technical or quantitative field (preferred).
5-7+ years of experience in AI/ML, data science, or software engineering delivering production AI solutions.
Strong Python and SQL skills with experience building GenAI and agentic AI applications using LangChain, LangGraph, or CrewAI.
Experience building GenAI applications with LLMs, RAG, prompt engineering, embeddings, vector databases, and LLM evaluation.
Experience designing agentic AI workflows with tool calling and multi-step orchestration using frameworks like LangChain, LangGraph, AutoGen, or CrewAI.
Experience with machine learning and data processing using scikit-learn, PyTorch, Pandas, or Spark.
Experience integrating AI solutions using APIs (REST/FastAPI), cloud platforms (AWS Bedrock, Azure AI, or Google Vertex AI), and tools like Git, Docker, or Kubernetes.
Experience with at least one major cloud AI platform such as AWS Bedrock, Azure AI Foundry, or Google Vertex AI.
Strong client-facing skills to translate business challenges into technical solutions for technical and non-technical audiences.
Experience with LLM fine-tuning, AI evaluation and governance (preferred).
Industry experience in high tech, electronics, or manufacturing (preferred).
Background in forward deployed engineering, solutions engineering, or client-facing consulting (preferred).
Front-end development experience, cloud/AI certifications, or open-source AI contributions (preferred).