pulsESG

• Develop and execute a comprehensive product strategy that integrates AI-powered capabilities with sustainability regulations, frameworks, and standards across our Sustainability AI platform. • Partner with applied AI engineers to scope, design, and ship LLM-powered features (e.g., agent workflows, retrieval-augmented generation, intelligent assistants for Sustainability reporting). • Use AI-assisted coding tools (e.g., Claude Code, Cursor, Copilot) to independently build working prototypes, internal tools, and proof-of-concept AI applications that accelerate discovery, validate ideas with customers, and de-risk engineering investment. • Stay abreast of evolving Sustainability regulations, frameworks, and standards at the global, regional, and industry levels, and ensure that our platform remains compliant with relevant requirements. • Shape product capabilities for greenhouse gas (GHG) accounting across Scopes 1, 2, and 3, decarbonization target-setting and tracking (e.g., SBTi, CDP), and audit-ready data lineage—ensuring our platform produces calculations and disclosures that hold up to third-party assurance. • Stay abreast of Sustainability frameworks, regulatory trends, and emerging applications of AI in sustainability reporting, and contribute to thought leadership initiatives through blog posts, webinars, and industry events. • Serve as a subject matter expert on sustainability regulations and standards, providing guidance and support to clients during onboarding, training, and ongoing usage of the platform. • Collaborate closely with clients, stakeholders, and internal teams to gather requirements and understand the needs of various user groups related to Sustainability compliance and reporting. • Work closely with engineering, design, and other cross-functional teams to prioritize features and enhancements based on customer feedback, market trends, and business objectives. • Lead the end-to-end product development lifecycle, from conceptualization and design to implementation and release, ensuring timely delivery of high-quality features and updates. • Conduct thorough market research to identify emerging sustainability regulations, frameworks, and standards as well as the rapidly evolving landscape of AI tools, models, and frameworks relevant to sustainability software, and assess competitor offerings and industry trends. • Establish key performance indicators (KPIs) and AI evaluation frameworks (quality, accuracy, consistency, failure modes) to measure the success and impact of AI-powered and Sustainability-related features, and use data-driven insights to drive continuous improvement. • Foster strong relationships with cross-functional teams, including sales, marketing, customer success, and legal, to ensure alignment and support for Sustainability-related initiatives.

• 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

• 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 • 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 • Translate ambiguous requirements into working solutions • Partner with product, design, and customer-facing teams to deliver impactful features • Collaborate effectively with distributed and offshore engineering teams