Applied AI/ML Lead

Be an integral part of a innovative and forward thinking agile team to enhance, build, and deliver advanced technology products. As an Applied AI/ML Lead within the Corporate Sector, Trade Surveillance Technology team, you will be responsible for delivering AI/ML enabled analytical and technical solutions that support the market surveillance and regulatory compliance capabilities. You will work across the full model lifecycle—from problem framing and data exploration to model development, productionization, and monitoring, partnering closely with data science and engineering teams. You will translate ambiguous business needs into robust, production-grade AI systems while championing sound engineering and responsible AI practices. This is a hands-on technical role with growing scope for technical leadership, mentorship of junior engineers, and influence over architectural decisions. Job Responsibilities Model development: Design, train, evaluate, and fine-tune machine learning, deep learning, and large language models (LLMs) to address defined business use cases. Productionization (MLOps): Build and maintain scalable, reliable ML/Feature/Data pipelines for training, deployment, inference, and monitoring in production environments. Data engineering collaboration: Work with large, complex datasets—performing feature engineering, data validation, and preprocessing to ensure model quality and reproducibility. Applied research: Stay current with advances in AI/ML (e.g., generative AI, RAG, agentic frameworks) and prototype new techniques to evaluate their applicability. System integration: Integrate models and AI services into applications via APIs, ensuring performance, latency, and cost efficiency. Quality and governance: Implement testing, evaluation frameworks, and monitoring to detect drift, bias, and degradation; adhere to responsible AI and model risk standards. Cross-functional partnership: Collaborate with data scientists and software engineers to scope requirements and deliver end-to-end solutions. Documentation and mentorship: Produce clear technical documentation and provide guidance and code review support to junior team members. Lead projects end to end: Guide and lead projects from understanding and establishing requirements, to design and final implementation/testing/delivery Required Qualifications, Capabilities, and Skills Master's degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, or a related quantitative field (or equivalent practical experience) and 6+ years of hands-on experience building and deploying ML/AI models in production settings. Strong programming proficiency in Python/SQL/Relational Databases/Linux and familiarity with AI/ML frameworks such as scikit-learn, PyTorch, SmartSDK for Agent building, base libraries such as pandas, numpy, etc. Solid understanding of ML fundamentals: supervised/unsupervised learning, deep learning, model evaluation, and optimization with experience with generative AI / LLMs, including prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG). Proficiency in MLOps tooling and practices: containerization (Docker), orchestration (Kubernetes), CI/CD, model versioning, and pipeline tools (e.g., MLflow, Kubeflow, Airflow) with experience with internal Cloud environments such as Gaia and GKP, and creating pipelines to deploy AI/ML models onto these platforms Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and its ML services with strong data skills: SQL, data pipelines. Familiarity with software engineering best practices: version control (Git), testing, code review, and API design along with experience with GenAI Gateway services integration for LLM based solution and Phoenix integrations for telemetry/eval/cost analysis Preferred Qualifications, Capabilities, and Skills Experience with vector databases, agentic AI frameworks, or LLM evaluation methodologies. Exposure to responsible AI, model risk management, or regulated-industry AI deployment. Working with distributed data processing (e.g., Spark) is a plus.