Senior/Lead AI engineer

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. They have 8 years of experience and leadership in enterprise AI, supported by expertise in data, analytics, cloud & DevOps, application modernization, and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.

• 5+ years of commercial software development experience with production-grade code in both Python and TypeScript.
• Strong hands-on experience integrating major LLM APIs such as OpenAI and Anthropic Claude.
• Deep expertise in meta-prompting, advanced prompt engineering, and context window optimization.
• Practical experience building and deploying Retrieval-Augmented Generation (RAG) systems using vector databases (Pinecone, Weaviate, ChromaDB).
• Hands-on experience with LLM orchestration frameworks like LangChain or LlamaIndex and embedding models.
• Experience developing automated testing, evaluation, and observability pipelines for AI/LLM applications.
• Proficiency in containerization with Docker, API design, and CI/CD automation pipelines.
• Experience as a Senior/Lead Developer or Technical Mentor in a fast-paced R&D environment.
• Excellent problem-solving skills and English communication abilities.

Nice to have:

• Experience with multi-agent architecture, agent orchestration, and progressive disclosure patterns.
• Practical knowledge of Model Context Protocol (MCP) or emerging context-sharing standards.
• Hands-on experience with LLM-as-a-Judge evaluation methodologies and A/B testing for Generative AI.
• Experience building developer tools, CLI utilities, or SDKs.
• Familiarity with OAuth/OIDC security and enterprise authentication protocols.

• Design, build, and deploy production-grade LLM-powered services, microservices, and APIs using Python and TypeScript.
• Implement advanced meta-prompting frameworks and prompt optimization techniques for deterministic, context-aware AI outputs.
• Develop scalable Retrieval-Augmented Generation (RAG) pipelines and integrate vector databases such as Pinecone, Weaviate, or ChromaDB.
• Orchestrate LLM workflows using frameworks like LangChain or LlamaIndex to connect multi-agent tools and domain knowledge.
• Implement automated testing, LLM evaluation, and quality assurance to benchmark model performance and accuracy.
• Build containerized microservices and CI/CD pipelines using Docker for deployment and sandboxed execution.
• Act as a hands-on Senior Developer and Mentor, guiding mid-level engineers, establishing coding standards, and conducting code reviews.

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