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Role Description

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

Qualifications

• 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

Requirements

• 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