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Ai Engineer

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NTT DATA

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Location:
Romania , Bucuresti

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

Not provided
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Job Responsibility:

  • 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

Requirements:

  • 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) or LangGraph
  • 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:

  • 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.)
What we offer:
  • Smooth integration and a supportive mentor
  • Pick your working style: choose from Remote, Hybrid or Office work opportunities
  • Our projects have different working hours to suit your needs
  • Sponsored certifications, trainings and top e-learning platforms
  • Private Health Insurance
  • Individual coaching sessions or accredited Coaching School
  • Epic parties or themed events

Additional Information:

Job Posted:
May 17, 2026

Employment Type:
Fulltime
Work Type:
Remote work
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