Uses agentic and generative AI platforms (LangChain, LangGraph, CrewAI) to build production AI workflows and developer-facing AI services; focused on building AI dev tools and integrations.
About the Role
Client-facing Forward Deployed AI Engineer who designs, builds, and deploys production-grade generative and agentic AI solutions for enterprise customers, bridging business requirements and engineering to scale AI-powered applications and automation.
Job Description
Role
A Forward Deployed AI Engineer responsible for partnering with enterprise customers to design, build, deploy, and scale production-grade AI solutions focused on Generative AI, Agentic AI, LLMs/SLMs, and RAG. The role acts as a bridge between business stakeholders and engineering teams to deliver AI-powered applications, intelligent automation, and modern AI platforms.
Key Responsibilities
- Design, develop, and deploy enterprise AI solutions leveraging Generative AI, Agentic AI, LLMs/SLMs, and RAG.
- Work directly with customers to understand business problems and translate them into scalable AI solutions.
- Develop AI-powered applications for conversational AI, intelligent automation, and AI-assisted SDLC.
- Build production-ready AI workflows using platforms such as LangGraph, CrewAI, LangChain, Azure AI Foundry, AWS Bedrock, and Google Gemini Enterprise.
- Design agent orchestration frameworks integrating enterprise systems, APIs, databases, and external tools.
- Optimize AI solutions for accuracy, latency, scalability, reliability, and cost efficiency.
- Build robust APIs, microservices, and AI services following software engineering best practices.
- Collaborate across Product, Engineering, Data Science, and DevOps; participate in customer workshops, PoCs, technical discussions, and deployments.
- Evaluate emerging AI technologies and contribute to continuous improvement.
Requirements
- Senior-level experience in Software Engineering, AI Engineering, or Machine Learning (posting lists “713 years” of experience; interpreted as senior/multiple years).
- Strong programming expertise in Python.
- Hands-on experience with LLMs, Agentic AI, Generative AI, RAG, Prompt Engineering, and AI Agents.
- Experience with orchestration frameworks such as LangGraph, LangChain, CrewAI, LlamaIndex, or similar.
- Familiarity with AI platforms like AWS Bedrock, Azure AI Foundry, and Google Gemini Enterprise.
- Experience with PyTorch, TensorFlow, and Hugging Face or other modern AI frameworks.
- Understanding of vector databases, embeddings, semantic search, and AI workflows.
- Experience with Docker, Kubernetes, REST APIs, CI/CD, and cloud platforms (AWS, Azure, or GCP).
- Strong communication skills and experience working directly with enterprise clients.
- Experience working in Agile development environments.
Preferred Skills
- Experience with MLOps/LLMOps and AI deployment frameworks.
- Exposure to PEFT, LoRA, QLoRA, or model optimization techniques.
- Knowledge of distributed computing and enterprise AI infrastructure.