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

Our benefits:

Relocation support to one of our hubs — Cyprus, Serbia, Spain, Georgia or Kazakhstan — with assistance for the employee and their family
Flexible work from one of our offices or remotely within time zones from GMT-2 to GMT+5
Healthcare Coverage
Education Budget: Language lessons, professional training and certifications
Wellness Budget: Mental health and fitness activity reimbursements
Vacation policy: 20 days of annual leave and paid sick leave

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