Forward Deployed Engineer – AI

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences. As a Forward Deployed Engineer – AI, you will work directly with clients to transform complex business challenges into production-ready AI solutions. Combining software engineering, data science, GenAI, agentic AI, and advanced analytics, you will rapidly prototype, deploy, and scale solutions that deliver measurable business value. Your primary responsibilities will include: Solve Client Challenges: Partner with business and technical teams to identify opportunities, define AI use cases, and establish success metrics. Build and Deploy AI Solutions: Develop end-to-end GenAI, RAG, and agentic AI applications using tools such as LangGraph, CrewAI, and hyperscaler cloud AI platforms (e.g., AWS, Google, Azure). Engineer Agentic and GenAI Systems: Design and implement LLM-powered agents, tool use and orchestration, prompt and context engineering, and evaluation frameworks using platforms such as LangGraph, CrewAI, Vertex AI, Azure AI Foundry, or Amazon Bedrock. Apply guardrails, observability, and testing to ensure accuracy, safety, and reliability. Data Engineering and Integrations: Prepare structured and unstructured data and integrate AI solutions with enterprise systems, APIs, and data platforms. Scale and Operationalize AI: Apply MLOps/LLMOps, testing, monitoring, and responsible AI practices to ensure solutions are reliable, secure, and production-ready. Drive Business Impact: Present insights, demonstrate outcomes, support client adoption, and turn successful solutions into reusable assets for future engagements. This job can be performed from anywhere in the US. 5+ years of experience in AI/ML, data science, or software engineering, delivering production AI solutions. Strong Python and SQL skills, with hands-on experience building GenAI and agentic AI applications, including LLMs, RAG, and multi-step workflows using tools such as LangChain, LangGraph, or CrewAI. Hands-on experience building GenAI applications with large language models, including RAG, prompt and context engineering, embeddings and vector databases (e.g., Milvus, Elasticsearch, pgvector, Pinecone), and LLM evaluation. Experience designing and building agentic AI workflows using frameworks such as LangChain/LangGraph, CrewAI, AutoGen, including tool calling and multi-step orchestration. Experience with machine learning and data processing using tools such as scikit-learn, PyTorch, Pandas, or Spark. Experience integrating and deploying AI solutions using APIs (REST/FastAPI), cloud AI platforms (AWS Bedrock, Azure AI, or Google Vertex AI), and tools such as Git, Docker, or Kubernetes. Experience with at least one major cloud AI platform (AWS Bedrock, Azure AI Foundry, or Google Vertex AI). Strong client-facing skills, with the ability to translate business challenges into technical solutions and communicate with technical and non-technical audiences. Demonstrable ability to deploy solutions to production for critical business areas Ability to understand business needs and drive technical solutions Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field. Master’s or PhD in a technical or quantitative field. Experience with LLM fine-tuning, AI evaluation and governance, Industry experience in high tech, electronics or manufacturing. Background in forward deployed engineering, solutions engineering, or client-facing consulting. Front-end development experience (e.g., React or Streamlit), cloud/AI certifications, or open-source AI contributions. United States Data & Analytics Hybrid Professional Multiple Cities PARAMUS CORAL GABLES Denver Charlotte Raleigh Boston Atlanta LOS ANGELES San Jose Dallas Houston Austin San Francisco WASHINGTON Cambridge Chicago New York (0147) International Business Machines Corporation Amazon Web Services Apache Kafka Artificial Intelligence Computer Science Consulting Data Science Databases DevOps Docker Engineering Financial Services Construction Framing Git Healthcare Jenkins Life Sciences Machine Learning Manufacturing Mathematics Delivery Pipelines Computer Programming Publications Python Representational State Transfer Structured Query Language Scheduling Semiconductors Shipping Spark Statistical Modeling Statistics Testing Writing Azure Google Cloud Pricing Vertex Kubernetes Microservices Development React Data Engineering Software Engineering Continuous Integration/Continuous Delivery ElasticSearch NumPy Pandas Library PyTorch Red Hat Scikit-learn Tensorflow Unstructured Data Large Language Model OpenShift Solutions Engineering Governance Supply Chain Mathematical Optimization Discrete-event Simulation GitHub Actions Bias Musical Orchestration FastAPI Foundry Gurobi LangChain Langgraph Religious Vocation Language Models Machine Learning Operations Large Language Model Operations AWS Bedrock CrewAI Model Risk Management Tooling Automated Content Generation Small Language Models XGBoost Library