Who We Are
Based in The Romania Excellence Centre, Bucharest - our client is seeking for experienced professionals who value teamwork, pioneering technology, and innovation. People who want to take their careers to the next level of success. You will be part of a global and diverse team, contribute to all stages of software development lifecycle and lead the implementation, deployment and test of multi-agent systems.
Also, this role will give you the chance to:
• Use frameworks like Google Agent Development Kit (Google ADK) and LangGraph to build robust, controllable, and observable agentic architectures
• Assist in the design of LLM-powered agents and multi-agent workflows (planning, tool use, orchestration, memory, and human-in-the-loop)
So if you are an experienced AI Engineer please 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
- 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
- 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)
- 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
- 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 or 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
- 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.)
