Job Expectations - Individual Contributor

Role Purpose :

We are seeking a Principal Technologist to lead a high-impact technology organization responsible for a portfolio of risk, governance, data, and analytics platforms. This role requires a strategic, hands-on technology leader with strong people leadership, proven delivery execution, and a passion for leveraging AI and Machine Learning to drive innovation, scale, and operational efficiency.

In this role, you will lead a results-driven engineering and data organization, serve as a key technology partner to senior business leaders, and own the delivery of complex, enterprise-critical initiatives. The scope includes application development, data platforms, advanced analytics, and modern cloud-based architectures. You will also play a critical role in shaping multi-year business and technology roadmaps and ensuring consistent execution against strategic priorities.

Key Responsibilities :

AI & Data Vision and Strategy :

- Define and execute the enterprise technology strategy across risk, governance, regulatory, policy, and enterprise oversight platforms, aligned with firm-wide AI-first and data-centric priorities.

- Identify and incubate next-generation AI, ML, and GenAI capabilities (LLMs, RAG, agents) applicable across service lines.

- Influence long-term technology investments, accelerators, and platform roadmaps; build and maintain multi-year business and technology roadmaps ensuring delivery of aligned initiatives.

Enterprise Architecture & Innovation :

- Establish reference architectures and best practices for AI, GenAI, analytics, and data platforms at enterprise scale, ensuring governance, controls, auditability, and regulatory compliance.

- Lead solutioning for high-complexity, multi-tower, multi-geography programs.

- Drive adoption of responsible AI, governance, security, and compliance standards.

Client Advisory & Thought Leadership :

- Serve as a trusted technology advisor to senior business leaders, understanding their priorities and translating them into reliable, scalable, and governed platform outcomes.

- Communicate with clarity and empathy across all levels, from engineering teams to executive stakeholders, producing high-quality materials that build confidence and alignment.

- Support strategic deals, GTM initiatives, and large transformation pursuits; represent the organisation in external forums and conferences.

Engineering Excellence & Enablement :

- Mentor senior architects, data scientists, and AI engineers across the firm; Provide overall leadership and accountability for a portfolio of applications, data platforms, and analytics capabilities, ensuring reliability, scalability, and security.

- Drive MLOps / LLMOps maturity, reuse, and industrialization of AI solutions.

- Contribute to patents, whitepapers, internal IP, and technical communities; represent the firm in industry forums, innovation councils, and analyst briefings.

- Demonstrate hands-on engineering rigour: capable of coding, debugging, and demonstrating architecture as code; participating in first-mile delivery and last-mile support.

- Engineering mindset with proximity to code - prioritizing technical skills and hands-on experience over a purely leadership or programme management background.

Delivery & Organizational Leadership :

- Lead a results-driven engineering and data organization; serve as a key technology partner to senior business leaders and own the delivery of complex, enterprise-critical initiatives.

- Lead resource strategy, staffing, and capacity planning across delivery and support teams (FTEs and contractors) to ensure effective execution and platform stability.

- Manage a delivery neighborhood consisting of multiple pods, maintaining accountability for outcomes, delivery cadence, and business priorities.

- Manage third-party vendor engagements, including scope definition, contract terms, delivery performance, and financial management.

- Work effectively within a matrix organisation - collaborating across core engineering teams, business leadership, and peers across geographies, balancing both leadership and service capabilities.

- Drive Programme leadership, cross-functional influence, stakeholder management, benefits realization, and executive reporting.

- Build and maintain multi-year business and technology roadmaps and ensure the successful delivery of aligned initiatives.

Soft Skills :

- Strong analytical and problem-solving skills, with an innovative and pragmatic approach to complex challenges.

- Proven ability to collaborate, influence, and build consensus across diverse business and technology stakeholders.

- Excellent verbal and written communication skills, including the ability to produce clear, high-quality executive-level documents.

Enterprise Competencies :

Learning Agility :

1. Quickly acquires AI literacy, keeps pace with emerging technologies (MLOps, LLM Ops, AI Ops), and continuously integrates new knowledge into platform strategy and engineering practice.

2. Adapts readily to evolving business domains - including risk, finance, and compliance - translating new understanding into effective technology solutions.

Customer Centricity :

1. Acts as a true technology partner to senior business leaders - understanding their priorities and translating them into reliable, scalable, and governed platform outcomes.

2. Communicates with clarity and empathy across all levels, from engineering teams to executive stakeholders, producing high-quality materials that build confidence and alignment.

Required Experience & Expertise :

- 20+ years in software engineering, data, analytics, and AI roles.

- Proven delivery of enterprise-scale AI and data platforms.

- Deep expertise in AI/ML, GenAI (LLMs, RAG, agents), data engineering, and cloud architecture.

- Strong understanding of multi-industry business problems and consulting dynamics.

- Industry recognition through patents, publications, or thought leadership preferred.

- Strong technical background spanning software engineering, cloud technologies, data engineering, analytics platforms, and application management.

- Experience with AWS and modern data engineering technologies such as Apache Spark, Apache Kafka, Python, SQL, and microservices-based architectures.

- Experience with analytics and visualization tools (e.g., Python, SQL, Tableau) strongly preferred.

- Demonstrable success in SDLC or TechOps modernization, preferably with experience in AI, automation, MLOps, LLM Ops, AI Ops, observability, and reliability engineering, along with strong data skills to ensure data readiness for AI solutions.

- End-to-end delivery experience in compliance - with the ability to understand and integrate business processes into technology solutions, rather than focusing solely on technical delivery.

Education :

- Required: Bachelor's degree in Computer Science, Engineering, or a related technical discipline, or equivalent professional experience.

- Preferred: Advanced degree (Master's or PhD) in a relevant technical or scientific field.