AI Engineer

Project Description:

VR-120391

• Join our Development Center in Bucharest, and become a member of our open-minded, progressive and professional team. In this role you will be working on projects for one our world famous clients.
• You will have a chance to grow your technical and soft skills, and build a thorough expertise of the industry of our client.
• On top of attractive salary and benefits package, Luxoft will invest into your professional training, and allow you to grow your professional career.
• Responsibilities:

Role Summary:

• • Work in a scaled Agile working environment
• • Be part of a global and diverse team
• • Contribute to all stages of software development lifecycle
• • Participate in peer-reviews of solution designs and related code
• • Maintain high standards of software quality within the team by following good practices and habits
• • 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)
• • Lead the implementation, deployment and test of multi-agent systems
• • Mentor junior engineers on best practices for LLM engineering and agentic system development
• • Drive technical discussions and decisions related to AI architecture and framework adoption
• • Proactively identify and address technical debt and areas for improvement in AI systems
• • Represent the team in cross-functional technical discussions and stakeholder meetings

Key Responsibilities:

• • 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

Mandatory Skills Description:

• Education & experience
• • 3+ years of experience as Software Engineer / ML Engineer / AI Engineer, with at least 1-2 years working directly with LLMs in real applications (not just experiments or coursework)
• • Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience)

Core technical skills

• 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:

• o Google Agent Development Kit (ADK) - building multi-agent workflows, using its orchestration, tools, and evaluation features

• o 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)

Soft skills:

• • Ability to translate ambiguous business requirements into concrete technical designs
• • Strong communication skills; able to explain trade-offs to both technical and non-technical stakeholders
• • Comfort working in an experimental environment with rapid iteration, but with a strong bias towards production quality and maintainability

Nice-to-Have Skills Description:

• 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.)

• Background in one or more of:

• • Information retrieval / search

• • NLP (beyond LLMs): classic text processing, embeddings, semantic similarity

• • Security & compliance for AI systems (PII handling, access control, audit logging)

• • Contributions to open-source AI projects, blog posts, or talks about LLMs/agentic systems

Language:

• English: English: B2 Upper Intermediate

• Romanian: C1 Advanced