The job in short
Are you passionate about exploring cutting-edge advancements in AI and software engineering? Do algorithms excite you, and do you thrive on tackling complex, data-driven challenges? If you're a true team player who’s ready to roll with a crew of experts, we want you on our squad!
Join us as an AI Engineer and be part of a dynamic team dedicated to creating innovative AI solutions on the Investment that will transform the banking landscape. Apply now and let’s make some magic happen!
Meet the job
Responsibilities:
- Design, develop, and deploy AI driven platform capabilities, part of a state of the art orchestration and automation platform;
- Analyze available data for training machine learning models or fine-tuning LLMs;
- Leverage composable frameworks for implementing AI driven capabilities as a single service or as an agentic flow;
- Collaborate with other experts and stakeholders to understand functional and non-functional requirements and translate them into scalable solutions;
- Optimize deployed AI capabilities, ensuring optimal performance in high demanding environments;
- Share knowledge in the team and across other value streams.
How about You
- Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 3-4 years of engineering experience;
- Strong experience in software engineering, designing modular systems, design patterns and coding best practices;
- Value-driven, to craft solutions that bring value to customers;
- Proven problem-solver, fast learner, resiliency;
- Proficiency experience in Python 3 (3.9+) and experienced using Python web frameworks such as FastAPI / Flask / Django;
- Familiarity with AI agents concepts: LLMs, Knowledge bases / Memory, Reasoning, States, Agent Tools,etc;
- Strong communication and collaboration skills;
- Knowledge in software architectures and event driven architectures;
- Experience shipping applications to production;
- Knowledge in designing SDLC through CI / CD flows, using any tool;
- Some experience in coding agents using some of the mainstream AI frameworks;
- Practical experience with LLM applications: prompt engineering, multi agent pipelines / workflows, RAG workflows, etc;
- Experience working with cloud computing platforms (e.g., AWS, Azure, GCP);
- Knowledge with AI context specific components and its particularities: LLMs, Vector databases, MCPs;
- Exposure to MLOps/LLMOps workflows: prompt versioning, CI/CD pipelines, trace logging, and automated LLM evaluations;
- Experience with monitoring, tracing and logging (observability stacks) tools.
