Senior Forward Deployed Engineer – AI

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.

Responsibilities

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.

Required qualifications

8-12+ 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.

This job can be performed from anywhere in the US.