Tech Lead ML Engineer (Internal R&D)
We are expanding our internal R&D department and building a cutting-edge proprietary platform focused on meta-prompting, context engineering, and centralized knowledge management for AI coding agents. This is a 100% internal R&D initiative (not an external client project), giving our team full ownership of the architecture, tech stack, and product vision.
Our platform provides structured context—rules, skills, workflows, and sub-agents—guiding AI systems to operate with a deep understanding of system architecture, domain constraints, and engineering standards. By reverse-engineering architecture and domain context, the system significantly accelerates project onboarding and drastically improves the reliability and consistency of AI-generated code.
We are looking for a hands-on Tech Lead ML Engineer to join our fast-paced R&D team. You will be responsible for architecting and implementing production-grade, LLM-powered systems from the ground up, while actively mentoring and elevating our engineering team.
CRITICAL REQUIREMENT: You must have a strong understanding and practical experience in Meta-prompting (controlling and guiding the model through a formalized language).
Essential functions
Architecture & Development: Lead the end-to-end design, architecture, and hands-on implementation of LLM-powered systems, writing scalable code in Python and TypeScript.
Meta-Prompting & AI Strategy: Design, test, and optimize advanced meta-prompts, context engineering workflows, and system rules to strictly guide AI coding agents.
System Integration: Build and optimize RAG pipelines, integrate Vector Databases, and orchestrate complex workflows using frameworks like LangChain or LlamaIndex.
Technical Leadership & Mentoring: Guide, mentor, and upskill junior to mid-level developers. Conduct code reviews, set engineering standards, and foster a culture of technical excellence.
MLOps & Quality Assurance: Establish and maintain automated testing, CI/CD pipelines, and robust evaluation systems (e.g., LLM-as-Judge) to ensure the reliability and safety of AI outputs.
R&D & Innovation: Continuously evaluate emerging GenAI models, tools, and orchestration protocols to keep our internal platform at the cutting edge of technology.
Qualifications
Core Engineering: Strong hands-on engineering skills in both Python and TypeScript, with a proven track record in API design, integration, and microservices architecture.
ML & LLM Mastery: Deep experience using top-tier LLM APIs (OpenAI, Anthropic Claude, etc.) combined with advanced prompt engineering and optimization techniques.
Architecture & Orchestration: Solid experience building RAG (Retrieval-Augmented Generation) systems, utilizing Vector Databases (Pinecone, Weaviate, ChromaDB, etc.), and working with LLM orchestration frameworks (LangChain, LlamaIndex, or similar).
Infrastructure & MLOps: Strong understanding of Docker, CI/CD pipelines, and a proven ability to implement automated testing and evaluation systems for AI models.
Leadership: Proven Tech Lead or senior mentoring experience—you know how to guide junior/mid-level developers and thrive in a fast-paced/startup environment.
Would be a plus
Experience with multi-agent systems and agent orchestration.
Knowledge of LLM-as-Judge evaluation methodologies.
Familiarity with MCP (Model Context Protocol) or similar emerging standards.
Experience implementing A/B testing frameworks specifically for AI systems.
Background in building developer tools or SDKs.
Deep knowledge of semantic search and embedding models.
Understanding of progressive disclosure patterns in AI applications.
OAuth/OIDC integration experience.
We offer
Opportunity to work on bleeding-edge projects
Work with a highly motivated and dedicated team
Competitive salary
Flexible schedule
Benefits package - medical insurance, sports
Corporate social events
Professional development opportunities
Well-equipped office
About us
Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment 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.
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