• Own end-to-end Machine Learning (ML) system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. • Architect and operate scalable inference systems, balancing latency, cost, and reliability. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate Machine Learning (ML) systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety

• Build and maintain full-stack web and mobile-friendly applications, including React-based front ends and their supporting data layers. • Design and maintain Airtable bases (schema, automations, interfaces, and data quality) that back internal and client-facing tools. • Build workflow automations in n8n and Zapier that connect the CRM, forms, databases, and other services. • Integrate third-party and AI services (for example, LLM APIs such as Anthropic's Claude) into applications and workflows where they add value. • Test, debug, and optimize applications for performance, reliability, and usability. • Collaborate with leadership, the Tech Project Manager, and external vendors to scope work and ship in tight, disciplined cycles. • Document systems and establish maintainable patterns so no single tool depends on one person.

Role Description We are looking for a highly skilled Machine Learning Engineer with strong expertise in both traditional machine learning and modern Generative AI (LLMs) to design, build, and deploy scalable AI solutions. The ideal candidate will work on large-scale structured and unstructured data, enabling intelligent automation and insights, particularly in domains such as insurance (claims, underwriting, fraud detection). Qualifications - Strong foundation in machine learning algorithms (supervised & unsupervised) - Experience with anomaly detection, time-series, and predictive modeling - Proficiency in Python and ML libraries (Scikit-learn, XGBoost, PyTorch/TensorFlow) - Hands-on experience with LLMs (OpenAI, Claude, Hugging Face, etc.) Requirements - Strong understanding of RAG (Retrieval-Augmented Generation) - Prompt engineering & evaluation - Embeddings & vector databases (e.g., FAISS, Milvus) - Experience building GenAI applications (chatbots, document summarization systems) - Experience working with large datasets (batch + streaming) - Knowledge of data pipelines and tools (Spark, Airflow, or similar) - Familiarity with cloud platforms (Azure, AWS, or GCP) - Experience deploying models using APIs (FastAPI/Flask) - Understanding of Docker, CI/CD pipelines, and model monitoring - Knowledge of version control and experiment tracking tools Benefits - Lead large-scale AWS-to-GCP cloud transformation initiatives. - Work on enterprise Data Lakehouse and analytics modernization projects. - Opportunity to migrate enterprise BI platforms from Qlik Sense to Looker. - Flexible remote work environment. - Exposure to global enterprise customers. - Collaborative, innovation-driven engineering culture. - Continuous learning and certification opportunities.

• Design, build, and maintain internal platforms and tools that serve People, Finance, Ops, Sales, and Engineering teams. • Own features, end-to-end requirements, architecture, implementation, testing, deployment, and monitoring. • Write clean, well-tested, production-grade code. • Build API-first integrations across the internal ecosystem connecting HRIS, CRM, finance platforms, knowledge management, and developer tools into a coherent stack. • Design for reliability, performance, and scale what you build today must hold as the company grows 5–10x. • Eliminate data silos. Build clean data pipelines that maintain a single source of truth across systems. • Own your services in production: monitoring, alerting, incident response, and post-mortems. • Build AI/LLM-powered features into internal workflows, automating approvals, knowledge retrieval, reporting, content generation, and operational processes. • Work directly with business stakeholders to understand pain points and translate them into technical solutions. • Pair with and mentor junior engineers. Raise the technical bar through code reviews, design reviews, and leading by example. • Propose architectural improvements, challenge assumptions, and drive best practices across the team.