AI Engineer

• Design, develop, and deploy AI driven platform capabilities, part of a state of the art orchestration and automation platform;
• 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.

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!, * 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.