AI Engineer Middle+ / Senior [Financial Assistant]

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About the team: We are growing the Financial Assistant team at Plata, building intelligent systems that support users in managing their finances, understanding spending, and interacting with financial products in a simple and intuitive way. This team plays a key role in improving customer experience, engagement, and the overall value of our core products. This is a customer-facing product in a regulated environment - accuracy, safety, and trust are non-negotiable. We use AWS, Go, Python and cloud-based models, yet still flexible to mix ready-made tools and ship our custom solutions. We're all about building systems that deliver real, valuable results for our organization. You will be a key specialist in a cross-functional team, working closely with backend, mobile and other LLM engineers. Challenges that await you: Work with agentic architectures where needed: tool use, multi-step reasoning, orchestration, and failure recovery Design and maintain eval infrastructure (offline test suites, golden datasets, regression harnesses) that gives a reliable signal after every change Work with advanced prompts, set up RAG-oriented data sources for efficient retrieval, and evaluate model outputs to ensure accuracy, relevance, and quality Implement safety and guardrails: hallucination detection, refusal strategies, factual grounding Run online and offline experiments ( canary, A/B) to validate that changes improve real user outcomes Own observability from day one: trace LLM calls, monitor quality drift, latency, and cost per session in production Collaborate with backend engineers and product to ship reliable improvements, review code, and maintain production health What makes you a great fit: Strong classical ML fundamentals, you understand what's happening inside models, not just how to call their APIs Strong eval mindset: you design the measurement system before writing the first prompt, and treat evals as a first-class engineering artifact Hands-on experience with search and retrieval: dense/sparse/hybrid, reranking, query understanding - using managed tooling effectively, not necessarily from scratch Practical experience with agentic architectures: tool use, orchestration, failure recovery Safety-first thinking: guardrails, content policies, graceful degradation under uncertainty, especially in a context where wrong answers have real consequences Production ML ownership: observability, latency budgets, cost tracking, regression detection Expertise in Python and its ecosystem, as well as language- and framework-agnostic mindset to achieve project goals Familiarity with open-source tools to build and evaluate RAG applications. Excellent communication skills and ability to explain complex technical concepts to a broad audience of stakeholders B1 or higher English level for effective communication with an international team Your bonus skills: Previous experience delivering business-critical ML/AI-powered applications for customer-facing products Fine-tuning or distillation of LLMs for production use cases Experience deploying open-source models (Llama, Mistral) in private/on-premise environments Experience in fintech, banking, or regulated domains Our ways of working: Innovative Spirit: A commitment to creativity and groundbreaking solutions Honest Feedback: valuing open, transparent communication Supportive Team: a strong, collaborative community Celebrating Achievements: recognizing our wins together High-Tech Environment: a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances

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