AI Engineer - United States

Pulsora•Chicago, IL

14h•Hybrid

About The Position

We are seeking a highly skilled and experienced AI/ML Developer to join our team. The ideal candidate will be a specialist in the rapidly evolving field of Large Language Models (LLMs) and generative AI, with a strong background in developing, integrating, and optimizing complex agentic and RAG-based systems. This role involves end-to-end implementation from frontend to backend, with a focus on leveraging AI for development and product enhancement. The position requires collaboration with team leads, managers, QA, and product managers, including a willingness to partially work US working hours.

Requirements

Undergraduate degree in Computer Science or similar Engineering field, advanced degree is a plus
5+ 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 and private/local, e.g., Gemini, OpenAI, Anthropic, Llama, Ollama, etc., including hands-on experience using techniques for LLM efficiency
At least 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.
Practical experience using tools like Claude Code, Cursor, OpenAI, Microsoft Copilot, including the ability to safely integrate AI-generated code into production (validation, CI/CD)
Solid understanding and implementation experience with RAG architectures, vector DBs, vector search, embeddings, and reranking mechanisms
Experience leveraging AI Copilot or similar generative AI coding tools for accelerated development, code generation, refactoring, optimization, and vibe coding to create integrated backend and frontend applications
Strong collaboration across a small team, including great communication with both onshore and offshore departments and translating vague requirements into actionable systems.
Self-motivated and proactive—you create momentum without needing direction
A strong problem solver who can navigate ambiguity
A builder who cares about shipping real, working products
Comfortable with ownership, accountability, and high expectations
Thrives outside of traditional work structures
You own the quality of the product you build

Nice To Haves

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 nice to have, but not necessary
Ideally have industry and market knowledge of ESG and sustainability

Responsibilities

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 and efficiency.
Develop and refine advanced prompt engineering techniques to maximize model performance, consistency, and safety.
Own end-to-end implementation from frontend to the backend.
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).