• Design, build and optimise AI-powered solutions, agents and workflows that address real business challenges and deliver measurable value. • Develop solutions using Microsoft technologies including Azure AI Foundry, Copilot Studio, Azure OpenAI and Microsoft Fabric. • Collaborate with business stakeholders, product teams and technical colleagues to translate requirements into scalable AI and data solutions. • Build and integrate data pipelines, APIs and enterprise data sources to enable intelligent, production-ready applications. • Apply software engineering, data engineering and AI best practices to ensure solutions are secure, scalable and maintainable. • Support the full solution lifecycle, including prototyping, testing, deployment, monitoring and continuous improvement. • Implement DevOps, MLOps and AIOps practices to enable reliable deployment, observability and operational excellence. • Contribute to the development of reusable AI capabilities, engineering standards and best practices across the AI Centre of Excellence. • Partner with Data Engineers, UX teams and business stakeholders to drive adoption and successful delivery of AI-enabled solutions. • Stay current with emerging AI technologies and identify opportunities to leverage new capabilities to improve business outcomes.

• Lead the delivery of AI-powered solutions from concept to production • Design, build and deploy AI workflows and automation solutions using Azure AI, Copilot Studio and Microsoft Fabric • Own the end-to-end delivery lifecycle, from requirements and solution design through to deployment and operational handover • Provide technical leadership and guidance to engineers, analysts and project teams • Drive continuous improvement by evaluating new technologies

Role Description As a Forward Deployed ML/AI Engineer, you will bridge the gap between cutting-edge AI research and robust, production-grade applications. You will be responsible for the end-to-end lifecycle of intelligent systems, from data ingestion and model training to deployment. This role requires deep technical proficiency in: - Training and tuning classical machine learning models (such as gradient-boosted trees, random forests, and regression suites) - Modern Generative AI architectures, including Large Language Models (LLMs), retrieval-augmented generation (RAG) pipelines, and agentic workflows Your mission goes beyond model development—you will own the end-to-end delivery of intelligent systems that directly impact customer business metrics. You will: - Design scalable APIs - Optimize model inference latency - Architect full-stack infrastructure to ensure AI capabilities are seamlessly delivered to end-users This role requires exceptional ability to navigate ambiguity, build trust with diverse stakeholders, and operate effectively in fast-paced, cross-functional customer environments. Qualifications - 6+ years of Machine Learning, SWE Gen AI or DS experience (must have productionized models) - 3+ years of implementation and customer-facing experience - In-depth knowledge of classical models (Scikit-Learn, XGBoost) and Generative AI architectures (LLMs, RAG pipelines, and Vector Databases) - Strong engineering skills in backend development (Python, FastAPI/Flask) and ML frontend frameworks (Streamlit) - Proven experience deploying, monitoring, and maintaining models in production (Docker, CI/CD pipelines) - Ability to translate business challenges into clear technical solutions, focusing on business outcomes and identifying root causes - Ability to explain technical concepts and trade-offs to executives in clear business terms - Experience building trust with customers, managing stakeholders, and working independently in fast-paced, ambiguous environments - Deep familiarity with tools like MLflow or Weights & Biases to track experiments, manage model packaging, and maintain an organized model registry - Experience setting up and managing AI/ML environments on cloud platforms (AWS, GCP, or Azure) - Background in building data pipelines, ETL processes, and working with SQL/NoSQL databases Requirements - Familiarity with reducing inference latency and managing compute costs (e.g., quantization, caching strategies) - Experience building autonomous AI agents or multi-agent orchestration frameworks Benefits - Ownership through equity participation - Annual company retreat - Education bonus for continuous learning - Company-wide winter break - Paid time off - Optional in-person events and meetups - Tailored career roadmaps - High-performance culture

• Deliver engaging, effective live sessions (virtual, via Microsoft Teams) covering AI-augmented full-stack development: agentic workflows, spec-driven development, Claude Code, GitHub Copilot, governance and compliance in AI-assisted engineering • Adapt delivery based on both evolving content (the underlying engineering platform is being built in parallel) and learner needs — reading the room to calibrate depth for beginners vs. more advanced participants • Review pre-work to understand where the cohort is struggling, then focus live time on the hard parts • Collaborate with SMEs and curriculum designers to stay current on what's being taught and why