Senior Machine Learning Engineer (Salary up to 130M Gross + Sign-on-bonus)

What do we do? 

As a pioneer for digital transformation, GFT develops sustainable solutions across new technologies – from cloud engineering and artificial intelligence to blockchain/DLT. With its deep technological expertise, strong partnerships, and comprehensive market know-how, GFT offers advice to the financial and insurance sectors, as well as the manufacturing industry. Through the intelligent use of IT solutions, GFT increases productivity and creates added value for clients. Companies gain easy and safe access to scalable IT applications and innovative business models. 

Who are we? 

Having started in Germany in 1987, GFT Technologies has grown to become a trusted Software Engineering and Consulting specialist for the international financial industry, counting many of the world’s largest and best-known banks as our clients. We are an organization that empowers you to not only explore but also raise your potential and seek out opportunities that add value. At GFT, diversity, equality, and inclusion are at the core of who we are. Ensuring a diverse and inclusive working environment for all communities is one of the main pillars of our diversity strategy, based on our core values and culture. We have been certified for 2022/23 as a ‘Great Place to Work’ in the APAC region. So, if you want to have the opportunity to work with an outstanding and progressive organization, this position could be right for you. 

Role Summary 

We are seeking a pragmatic Senior Machine Learning Engineer to accelerate our MLOps roadmap. Your primary mission will be to own the design and implementation of our V1 LLM Evaluation Platform, a critical system that will serve as the quality gate for all our AI features. You will be a key builder on a new initiative, working alongside dedicated Data Engineering and DevOps experts to deliver a tangible, high-impact platform. This role is for a hands-on engineer who thrives on building robust systems that provide leverage. You will be fully empowered to own the implementation and success of this project.

Sign-on Bonus: Eligible for candidates who are currently employed elsewhere and able to join GFT within 30 days of offer acceptance.

Key Responsibilities

• Build the V1 Evaluation Platform: Proactively own the end-to-end process of designing and building the core backend systems for our new LLM Evaluation Platform, leveraging Arize Phoenix as the foundational framework for traces, evaluations, and experiments.
• Implement Production Observability: Architect and implement the observability backbone for our AI services, integrating Phoenix with OpenTelemetry to create a centralized system for logging, tracing, and evaluating LLM behavior in production.
• Standardize LLM Deployment Pipeline: Design and implement the CI/CD framework for versioning, testing, and deploying prompt-based logic and LLM configurations, ensuring reproducible and auditable deployments across all AI features.
• Deliver Pragmatic Solutions: Consistently make pragmatic technical decisions that prioritize business value and speed of delivery, in line with our early-stage startup environment.
• Cross-functional Collaboration: Work closely with our Data Science team to understand their workflow and ensure the platform you build meets their core needs for experiment tracking and validation.
• Establish Core Patterns: Help establish and document the initial technical patterns for MLOps and model evaluation that will serve as the foundation for future development.

Required Qualifications

• 5+ years of dedicated software engineering experience, with a strong focus on backend or platform systems.
• Expertise in Python: You have a proven track record of building robust, testable, and maintainable production systems.
• Deep MLOps/LLMOps Experience: You have hands-on experience with the unique challenges of productionizing and evaluating modern ML/LLM systems, ideally with exposure to evaluation frameworks like Arize Phoenix, LangSmith, or similar platforms.
• Observability Expertise: Practical experience with modern observability frameworks, especially OpenTelemetry, is essential.
• Pragmatic Problem-Solving: A demonstrated ability to choose the right solution for the problem at hand, avoiding over-engineering while ensuring robustness.

Preferred Qualifications 

• Arize Phoenix Experience: Hands-on experience with Phoenix for LLM observability, including setting up custom evaluators, managing datasets, and implementing trace-based debugging.
• AWS Experience: Familiarity with core AWS services used in a platform context (Kubernetes/EKS, RDS, S3, IAM).
• Experience in a Startup Environment: Comfortable with ambiguity and a fast-paced setting.