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# Forward Deployed Engineer - AI

- **Company:** IBM
- **Location:** Cambridge, MA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Information Engineering, Elasticsearch, Python (Programming Language), Machine Learning, Open Source Technology, Standard Sql, Software Engineering, Systems Integration, Unstructured Data, Pinecone, Data Processing, Enterprise Software Applications, Pytorch, ReactJS, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Prompt Engineering, Apache Spark, Google Vertex AI, Generative AI, Agentic-AI, Git, Fastapi, Pandas, Build Management, Pgvector, AI Platforms, Scikit Learn, Kubernetes, Information Technology, Amazon Bedrock, CrewAI, AutoGen, Milvus, Data Management, Machine Learning Operations, Front End Software Development, Azure AI, Evaluation of Large Language Models, Streamlit Framework, Docker
- **Published:** October 16, 2026
- **Apply:** https://dejobs.org/x/x/439E626C6FB341C5858A0F06C2F54A81/job/

## About the Role

Required technical and professional expertise

 * 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.

Preferred technical and professional experience

 * 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.

## Description

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.