pulsESG

AI Engineer

• 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)

• Build & Ship AI-Powered Products
• Design, develop, and deploy LLM-powered applications and agent-based systems in production
• Own the full product lifecycle from concept → production → iteration across backend and frontend
• Build scalable, maintainable systems that solve real customer problems
• Design & Operate GenAI Systems
• Develop prompt strategies, context management (RAG, memory), and agent workflows
• Integrate and optimize across leading LLM APIs (OpenAI, Anthropic, Gemini, open-source)
• Continuously improve system quality, reliability, and performance
• Use AI-assisted coding tools (e.g., Cursor, Copilot, Claude Code) to accelerate development
• Validate, test, and safely integrate AI-generated code into production systems
• Own testing strategy including unit, integration, and end-to-end testing
• Define and implement evaluation frameworks for AI outputs (quality, consistency, failure modes)
• Debug production issues and optimize for latency, cost, and scalability
• Collaborate effectively with distributed and offshore engineering teams
• Build advanced agent systems (planning, orchestration, parallel execution)
• Explore fine-tuning, evaluation frameworks, and benchmarking techniques

AI Engineer

Applied AI Engineer