Design, develop, and deploy production-grade applications leveraging various LLMs, context optimization, etc.
Architect and implement sophisticated, multi-step and multi-agent workflows using frameworks like LangChain and LangGraph.
Build and optimize RAG pipelines, including implementing and managing embeddings, vector databases, and advanced rerankers to enhance response quality and relevance.
Use code generation applications (e.g. Replit, Cursor, Google AI Studio, Git Hub Copilot in Agent mode, etc.) to create full applications (including frontend and backend), generate tests, perform testing and integrate them in the core product without writing any code.
Lead efforts in LLM fine-tuning (e.g., LoRA, QLoRA) for specific domain knowledge and tasks, and implement strategies for efficiency.
Develop and refine advanced prompt engineering techniques to maximize model performance, consistency, and safety.
Do end-to-end implementation from frontend to the backend. Highly desired.
Expose AI/LLM functionality written in Python using Java services, leverage multi-threading capabilities in Java to augment AI/LLM functionality developed in Python.
Utilize AI-powered development tools (e.g., GitHub Copilot, etc.) to efficiently generate, refactor, and optimize high-quality code.
Work closely with team leads managers, QA, product managers and team in the US (this will require the willingness to partially work US working hours)

6+ years of professional experience in software development, with a minimum of 2 years focused on AI/ML development, particularly with LLMs.
Strong proficiency in Python and its relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn).
Proven experience integrating and working with major LLM APIs
both public LLms and private/local LLMs, e.g., Gemini, OpenAI, Anthropic, Llama, Ollama, etc.
1+ year of deep practical experience with LangChain and LangGraph for building complex LLM applications and agentic workflows using autonomous agents, tools, memory management, parallelization, etc.
Solid understanding and implementation experience with RAG architectures, vector DBs, vector search, embeddings, and reranking mechanisms.
Hands-on experience with techniques for LLM efficiency, fine-tuning, and context management.
Experience creating UI using React and integrating the UI with the backend using REST APIs is a big plus.
Hands-on experience with Java, especially integrating Java modules with Python modules is a big plus.
Experience leveraging AI Copilot or similar generative AI coding tools for accelerated development, code generation, refactoring, and optimization, vibe coding to create integrated backend and frontend applications.
Must have undergraduate degree in Computer Science or similar Engineering field, advanced degree a plus

AI/LLM Engineer

Key skills