LLM Engineer
Job Title: LLM Engineer
No. of Positions: 3
Location: Chicago, Princeton or NYC -USA(Hybrid 3 days Office)
Employment Type: Contract
About the Role
We are looking for a highly skilled LLM Engineer to design, develop, and optimize Large Language Model (LLM)-powered applications. This role requires deep expertise in LLM APIs, AI tool integrations, and vector database technologies. You will work with cutting-edge frameworks such as Lang Graph and DSPy, build tool-using AI systems, and leverage modern orchestration techniques to deliver intelligent, scalable, and high performance solutions.
Key Responsibilities
Design and develop LLM-powered applications integrating APIs such as Open AI, Claude, Gemini,etc.
Build AI workflows using Lang Graph, DSPy, and tool-use frameworks.
Implement MCP server integrations to extend LLM capabilities.
Work with vector databases such as Qdrant, Milvus, or Pgvector for semantic search and retrieval. Optimize prompt engineering, model orchestration, and tool chaining for performance and accuracy.
Collaborate with cross-functional teams to translate requirements into AI-enabled solutions.
Ensure solutions adhere to best practices in security, scalability, and maintainability.
Use Azure DevOps for code management, CI/CD pipelines, and deployment.
Participate in Agile ceremonies, sprint planning, and delivery reviews.
Required Skills & Qualifications
Bachelor s or master s degree in computer science, AI/ML, Engineering, or related field.
4+ years of experience in software development, with 2+ years in LLM/AI application development. Strong hands-on experience with LLM APIs (Open AI, Claude, Gemini, etc.).
Experience with Lang Graph, DSPy, and tool-use patterns in AI systems.
Knowledge of MCP server integration and usage.
Expertise in vector databases (Qdrant, Milvus, Pgvector).
Familiarity with Azure DevOps and Agile delivery methodologies.
Good to Have
Experience with RAG (Retrieval-Augmented Generation) pipelines.
Knowledge of MLOps practices and AI model deployment.
Familiarity with multi-cloud environments (Azure, AWS, GCP).
Exposure to containerization (Docker, Kubernetes).
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