AI Engineer

Key Responsibilities & Skills and Experience

Proven experience (13+ years) in an AI Engineer role, demonstrating successful application of AI skills and advanced techniques to solve complex, real-world problems, with a track record of leading technical initiatives. 

Extensive hands-on software engineering experience, including the design and delivery of enterprise-scale distributed applications and production-grade AI or Generative AI solutions. 

Deep expertise in Python or Java engineering skills and practical experience with API, microservices and asynchronous application design; experience with FastAPI, Spring Boot or comparable frameworks is beneficial. 

Strong experience with BigQuery and PostgreSQL, cloud-native architectures, Git-based development, CI/CD, Docker, Kubernetes, infrastructure as code, observability and automated testing; practical knowledge of GitHub or Azure DevOps, Terraform and OpenTelemetry is beneficial. 

Practical expertise in LLM applications, AI agents, RAG architectures, vector databases, semantic or hybrid search, model evaluation and LLMOps, with hands-on experience in relevant orchestration, evaluation and observability tools. 

Solid grounding in machine learning, feature engineering, model explainability and analytical modelling, using relevant Python ML libraries.

Strong understanding of financial markets and investment products is highly desirable, with experience in Investment Banking / Treasury.

Strong engineering mindset and ownership mentality, with sound judgement and the ability to structure ambiguity, solve complex problems, make pragmatic technical decisions and balance delivery speed, quality, risk, maintainability and long-term scalability. 

Excellent stakeholder management, communication and collaboration skills, with the ability to understand business needs, translate them into clear technical choices, manage expectations, influence decisions and build trusted relationships across business, product, technology and control functions. 

Demonstrated technical leadership, including the ability to set direction, lead by example, mentor and develop engineers, provide constructive challenge, drive accountability and foster an inclusive culture of learning, experimentation and engineering excellence. 

Ability to lead delivery across the engineering lifecycle, establish fit-for-purpose standards and working practices, manage dependencies and trade-offs, and maintain focus on measurable business and user outcomes. 

Bachelor’s degree in Computer Science, Engineering, Science or a related discipline, or equivalent professional experience

Strategic AI Solution Development: 

End-to-End AI Architecture & Leadership: Lead the design, architecture, and implementation of highly scalable and robust AI-powered applications and services utilizing Python, FastAPI/Flask, FastMcp LangGraph, ADK and Google VertexAI. 

Advanced Model Integration & Optimization: Architect, integrate, and expertly fine-tune large language models, with a strategic focus on the Gemini Model, for advanced NLP tasks and cutting-edge generative AI applications. 

Pioneering Conversational & Agentic AI:  Agentic AI System Orchestration: Architect and implement advanced Agentic AI patterns, orchestrating complex AI workflows using frameworks like ADK and LangGraph to build robust, intelligent, and autonomous systems. 

Scalable System Integration & Cloud Infrastructure: 

API Architecture & Integration: Drive the design and implementation of seamless API architectures and integrations with diverse internal and external systems, ensuring optimal data flow and functionality across the enterprise. 

Cloud Infrastructure Leadership: Lead the deployment, management, and optimization of AI solutions on cloud platforms, leveraging services like GCP CloudRun/GKE, BigQuery, Cloud Composer, and CloudSQL for resilient data processing and infrastructure management. 

Operational Excellence & Quality Assurance: 

Code Quality & AIOps Governance: Establish and enforce rigorous code quality standards and best practices for software development and AIOps, ensuring clean, maintainable, and well-documented code across all AI projects. 

Advanced Testing & Deployment Oversight: Architect and execute comprehensive test strategies for AI solutions, leading the deployment and continuous monitoring of models in production environments, ensuring high performance and reliability. 

Thought Leadership & Cross-Functional Collaboration: 

Research & Innovation Leadership: Actively lead research into the latest advancements in AI, machine learning, and natural language processing, translating cutting-edge developments into innovative solutions and strategic initiatives. 

Cross-Functional Strategic Partnership: Collaborate closely with product managers, data scientists, and other engineering leaders to define strategy, align priorities, and ensure the successful delivery of high-quality, impactful AI products that meet business objectives.