AI / ML Engineer

ROLE SUMMARY
We are looking for an AI/ML Engineer to join our R&D/Lab function and help turn emerging AI capabilities into real,
marketable products.
This role sits between AI research and product engineering. You will have room to investigate new models, agent
architectures, evaluation techniques, reasoning approaches, and emerging AI technologies — but the goal isn’t to
build technology simply because it is interesting.
We are looking for someone who can answer: “Is this technology useful enough to become a product, and can we
actually productionize it?
WHAT YOU'LL DO
– Research and evaluate emerging AI/ML technologies relevant to financial services.
– Prototype new approaches quickly and determine their practical value.
– Work with the Product and Engineering teams to turn promising research into production capabilities.
– Experiment with:
• LLMs and reasoning models
• Agent architectures
• RAG and retrieval
• Agent evaluation
• Model routing
• Fine-tuning and model adaptation
• Context engineering
• AI observability
• Cost/latency optimization
• Multimodal AI
• AI safety and guardrails
– Benchmark models and approaches using measurable evaluation criteria.
– Build proof-of-concepts that can evolve into production features.
– Develop reusable AI capabilities rather than one-off demos.
– Work with real financial-services use cases and customer problems.
– Partner with engineering to productionize successful experiments.
– Help define technical differentiation for our AI platform.
– Stay current on the rapidly evolving AI ecosystem and identify technologies worth adopting.
– Clearly communicate the business/product implication of research findings.
WHAT WE'RE LOOKING FOR
– 3–7+ years of experience in ML/AI/software engineering, depending on seniority.
– Strong Python skills and solid software-engineering fundamentals.
– Practical experience with LLMs and modern AI/ML systems.
– Experience with frameworks such as PyTorch, Hugging Face, LangChain/LangGraph or equivalent.
– Understanding of model evaluation and experimentation.
– Ability to build prototypes quickly but cleanly enough to transition into production.
– Strong analytical and problem-solving skills.
– Ability to read and understand research papers and translate relevant ideas into implementations.
– Strong communication and collaboration skills.
PARTICULARLY VALUABLE
– Experience with financial services or enterprise AI.
– Agentic AI experience.
– LLM evaluation/evals.
– Experience with production ML/AI infrastructure.
– Understanding of AI security, governance, explainability, and compliance.
– Experience taking an AI research idea from paper → prototype → product.

Work arrangement
No