AI Engineer

Overview

Who We Are Based in The Romania Excellence Centre, Bucharest - our client is seeking experienced professionals who value teamwork, pioneering technology, and innovation. You will be part of a global and diverse team, contribute to all stages of the software development lifecycle and lead the implementation, deployment and test of multi-agent systems. Also, this role will give you the chance to join our team to create the next big thing in digital banking.

What You’ll Be Doing

• Design and build complex agentic systems with multiple interacting agents
• Implement robust orchestration logic (state machines / graphs, retries, fallbacks, escalation to humans)
• Implement RAG pipelines, tool calling, and sophisticated system prompts for optimal reliability, latency, and cost control
• Apply core ML concepts to evaluate and improve agent performance, including dataset curation and bias/safety checks
• Lead the development of agents using Google ADK and/or LangGraph, leveraging advanced features for orchestration, memory, evaluation, and observability
• Integrate with supporting libraries and infrastructure (e.g., LangChain/LlamaIndex, vector databases, message queues, monitoring tools) with minimal supervision
• Define success metrics, build evaluation suites for agents (automatic + human evaluation), and drive continuous improvement
• Curate and maintain comprehensive prompt/test datasets; run regression tests for new model versions and prompt changes
• Deploy and operate AI services in production, establishing CI/CD pipelines, observability, logging, and tracing
• Debug complex failures end-to-end, identifying and document root causes across models, prompts, APIs, tools, and data
• Work closely with product managers and stakeholders to shape requirements, translate them into agent capabilities, and manage expectations
• Document comprehensive designs, decisions, and runbooks for complex systems

What We’re Looking For

• Bachelor’s degree in Computer Science, Engineering, or related field
• At least 3 years of experience as Software Engineer / ML Engineer / AI Engineer, with at least 1-2 years working directly with LLMs in real applications

Programming & Software Engineering

• Strong proficiency in Python (core language features, packaging, testing, async, type hints)
• Very strong software engineering practices: version control (Git), unit/integration testing, code reviews, CI/CD
• Experience building and consuming REST/gRPC APIs and integrating external tools/services

Machine Learning (Good Understanding)

• Understanding of core ML concepts: supervised/unsupervised learning, train/validation/test splits, overfitting, regularization, and common metrics (precision, recall, F1, ROC-AUC, etc.)
• Good understanding of deep learning basics (neural networks, embeddings) and at least one ML/DL framework (e.g., PyTorch, TensorFlow, JAX, scikit-learn)

LLMs & Agentic AI (Very Strong Understanding)

• Deep practical knowledge of large language models
• Tokenization, context windows, temperature, top-p, system vs user prompts
• Prompt engineering patterns (ReAct, chain-of-thought, tool-calling/tool-use)
• Fine-tuning / adapters / instruction-tuning, or experience with RAG as an alternative
• Experience building LLM-powered applications end-to-end: from idea → prototype → production
• Familiarity with safety and reliability considerations: hallucinations, guardrails, content filtering, privacy

Agentic Frameworks (Required Understanding, Experience Preferred)

• Conceptual understanding of modern agentic frameworks and patterns (stateful graphs, multi-agent coordination, human-in-the-loop, memory, and evaluation)
• Hands-on experience with at least one of:
• Google Agent Development Kit (ADK) – building multi-agent workflows, using its orchestration, tools, and evaluation features
• LangGraph – designing graph-based, stateful agent workflows with cycles, branches, and durable execution
• Candidates must be able to read, reason about, and extend ADK/LangGraph-based codebases
• Direct production experience with both ADK and LangGraph is a strong plus

Data & Infra

• Experience working with vector databases (e.g., Pinecone, Weaviate, pgvector, Chroma) for retrieval-augmented generation
• Comfortable with SQL and basic data modeling
• Experience deploying on at least one major cloud platform (GCP, AWS, Azure) and using managed services (e.g., serverless runtimes, container orchestration, secrets management)

Nice-to-Have Experience With

• Vertex AI / Gemini or other hosted LLM ecosystems
• Related frameworks and tools: LangChain, LlamaIndex, semantic search, evaluation frameworks (e.g., RAGAS, custom eval harnesses)
• Monitoring and observability stacks (OpenTelemetry, Prometheus/Grafana/NewRelic, Datadog, etc.)