AI Engineer, AS

Your key responsibilities

• AI Solution Development: Design, develop, and implement AI-powered applications and services using Python, FastAPI/Flask, and Google VertexAI.
• Model Integration & Fine-tuning: Integrate and fine-tune large language models, specifically the Gemini Model, for various NLP tasks and generative AI applications.
• Conversational AI: Develop and enhance conversational AI agents, focusing on natural language understanding and response generation.
• Agentic AI & Orchestration: Implement Agentic AI patterns and orchestrate complex AI workflows using ADK and LangGraph to create robust and intelligent systems.
• API Integration: Design and implement seamless API integrations with various internal and external systems, following REST principles.
• Cloud Infrastructure & Data Processing: Deploy and manage AI solutions on Google Cloud Platform (GCP), leveraging services like CloudRun/GKE, BigQuery, CloudSQL, Cloud Composer, and GCS.
• Code Quality & Best Practices: Write clean, maintainable, and well-documented code. Use Test-Driven Development (TDD), refactor constantly, and follow best practices for software development and AgentOps.
• Testing & Deployment: Develop and execute comprehensive tests for AI solutions and assist in the deployment and monitoring of models in production environments.
• Research & Innovation: Stay current with the latest advancements in AI, machine learning, and natural language processing to propose and implement innovative solutions.
• Collaboration: Work closely with cross-functional agile teams including product managers, data scientists, and other engineers to deliver high-quality AI products.

Your skills and experience

Essential Skills

• Professional Experience: 4+ years of proven experience in an AI Engineer role, demonstrating successful application of AI techniques to solve real-world problems.
• Programming Proficiency: Strong proficiency in Python (with libraries like scikit-learn, pandas, NumPy) and experience with web frameworks like FastAPI or Flask for building APIs.
• AI/ML Platforms: Hands-on experience with the Google VertexAI platform.
• Large Language Models (LLMs): Demonstrated experience working with and fine-tuning LLMs, particularly the Gemini Model.
• Conversational AI: Proficiency in developing conversational AI solutions.
• Agentic AI: Solid understanding of Agentic AI principles, design patterns, and familiarity with frameworks like LangGraph for orchestration.
• Cloud & Data Technologies: Strong experience with GCP services including CloudRun/GKE, BigQuery, and CloudSQL.
• Agile & DevOps: Experience working in an agile team (Scrum, Kanban) with a practical understanding of CI/CD tools (Jenkins, Github Actions, Teamcity) and AgentOps practices.
• Problem-Solving: Excellent analytical and problem-solving abilities with the ability to translate complex business requirements into technical solutions.

Ideal Qualifications

• Education: Bachelor’s, Master’s, or PhD in a relevant field such as Computer Science, AI/ML, Statistics, or Engineering.
• Infrastructure as Code: Familiarity with tools like Terraform.
• UI Frameworks: Experience with Flask or Streamlit for building user interfaces.
• Domain Knowledge: Experience in the financial services industry, particularly Investment Banking, is highly desirable.

How we’ll support you

• Training and development to help you excel in your career
• Coaching and support from experts in your team
• A culture of continuous learning to aid progression
  • A range of flexible benefits that you can tailor to suit your needs