Job DescriptionKEY OBJECTIVES:We are seeking a highly skilled AI Engineer to lead the design, solutioning, and development of scalable, reusable AI-driven core capabilities for our global Finance Transformation program. This role sits at the intersection of finance, technology, and governance, enabling the development of intelligent, compliant-by-design solutions that support key Finance functions at a global level.
You will partner closely with cross-functional teams including Finance, IT, Global Analytics, Legal, Compliance, and Risk to deliver secure, scalable, and regulatory-aligned AI solutions across the enterprise.
MAJOR RESPONSIBILITIES:- Lead the architecture, design, and development of AI/ML solutions at a global level to support finance and controllership processes (e.g., close, reconciliation, reporting, anomaly detection etc).
- Build scalable, reusable core AI capabilities that can be leveraged across multiple finance use cases and geographies.
- Translate business requirements into robust technical solutions, ensuring alignment with enterprise architecture and data strategy.
- Collaborate with Finance, IT, Data & Analytics, Legal, Compliance, Security, Enterprise Architecture and Risk teams to design and implement compliant-by-design AI solutions.
- Ensure adherence to regulatory requirements, data privacy laws, and internal governance frameworks.
- Develop and deploy models using modern AI/ML frameworks; ensure model performance, monitoring, and lifecycle management.
- Identify opportunities to automate and optimize finance processes using AI (e.g., intelligent automation, NLP, predictive analytics).
- Provide technical leadership and mentorship to junior engineers and cross-functional teams.
- Ensure high quality code that meets business objectives, quality standards and development guidelines.
- Building reusable pipelines, processes, and tools to streamline LLM and generative AI workflows while driving adoption of MLOps best practices, including CI/CD pipelines, versioning, testing, and model governance.
- Manage project stakeholder expectations and issue communications on progress.
- React to shifting priorities without compromising deadlines and momentum.
- Stay current with emerging AI technologies and assess their applicability within finance and risk-controlled environments.
QualificationsQUALIFICATIONS:- Must have:
- 2 - 5 years' experience in AI Engineering and/or Machine Learning (ML) with a focus on LLMs, with deep expertise in writing, and reviewing production code in Python
- Understanding the development lifecycle for LLMs- developing data sets for pre-training, instruction tuning, and preference alignment alongside the modelling techniques for each stage and LLM deployment is as MAJOR plus
- Strong knowledge of LLM frameworks and libraries (such as transformers, trl, deepspeed, PyTorch), and exposure to various ML techniques and their practical implementation in production at large scale
- Experience building and deploying solutions on cloud platforms (AWS, Azure, or GCP)
- Experience on distributed, high throughput and low latency architectures
- Strong fundamentals in NLP techniques for text representation, semantic extraction techniques, data structures and modeling
- Experience building software on top of major container technology (Kubernetes, Docker etc.)
- Knowledge of version control using Jenkins, GitHub Actions, GitLab CI, Jenkins, or Azure DevOps
- Solid understanding of data engineering concepts and working with large-scale datasets
- Experience implementing ML Ops practices and production-grade AI systems
- Familiarity with data privacy, model governance, and responsible AI principles
- Nice to have:
- Experience and Knowledge of Finance Domain: Understanding of finance concepts, workflows, or platforms is a strong asset for this role along with Knowledge of financial processes such as close, consolidation, reporting, and audit
- Exposure to regulatory and compliance frameworks (e.g., SOX, GDPR, model risk management)
- Experience with ERP systems (e.g., SAP, Oracle) and finance data ecosystems
- Experience defining system architecture and exploring technical feasibility tradeoffs is a plus
- Strong understanding of AI risk, explainability, and auditability
- Familiarity with end-to-end application development using full stack is a plus
- Experience in P&C insurance is a plus
- Key Competencies:
- Strong problem-solving and analytical thinking
- Ability to work across cross-functional and global teams
- Excellent communication and stakeholder management skills
- High attention to governance, risk, and compliance considerations
- Ability to balance innovation with control and scalability
- What Success Looks Like:
- Delivery of scalable AI capabilities embedded within finance processes
- Measurable improvements in key KPIS - efficiency, accuracy, and compliance
- Strong adoption of AI solutions across finance teams globally
- Robust governance and audit-ready AI implementations
