1 day ago
Sibiu, Romania +2 moreSenior
Responsibilities
- Assess AI use cases for feasibility, data readiness, value, operational constraints, risk, and build-versus-buy decisions.
- Define AI engineering architectures, reusable components, lifecycle standards, guardrails, and decision criteria.
- Design and implement AI solutions spanning data preparation, feature or context engineering, model selection, experimentation, training or fine-tuning, evaluation, serving, integration, and monitoring.
- Develop production predictive models, retrieval pipelines, embeddings, vector search, generative workflows, tool-use systems, and agentic orchestration.
- Establish reproducible experimentation, versioning, metadata capture, benchmarking, and model or prompt promotion criteria.
- Build MLOps or LLMOps pipelines for testing, packaging, security checks, deployment, rollback, promotion, and environment consistency.
- Create evaluation and monitoring frameworks covering performance, robustness, groundedness, hallucination, safety, bias, explainability, latency, scalability, cost, drift, usage, and operational outcomes.
- Apply security, privacy, responsible AI, governance, regulatory, access-control, provenance, human-oversight, and auditability controls.
- Lead technical reviews, proofs of value, architecture decisions, technology assessments, incident root-cause analyses, and performance investigations.
- Collaborate with application engineers to expose AI capabilities through reliable APIs, services, event flows, and user experiences.
- Document technical decisions, experiments, assumptions, limitations, evaluation evidence, dependencies, risks, and operational procedures.
- Mentor AI, data, and software engineers and provide technical leadership across engineering, architecture, platform, security, and governance stakeholders.
Requirements
- Deep knowledge of machine learning engineering, model lifecycles, statistics, supervised and unsupervised learning, deep learning, and production inference.
- Strong hands-on Python experience and familiarity with scikit-learn, PyTorch, TensorFlow, Hugging Face, or equivalent technologies.
- Advanced experience with generative AI, large language models, embeddings, retrieval-augmented generation, vector stores, prompt or context engineering, tool calling, and agent orchestration.
- Experience with data preparation, feature engineering, data quality, lineage, distributed processing, SQL or NoSQL stores, and secure enterprise-data access.
- Experience with MLOps or LLMOps, experiment tracking, model registries, pipeline orchestration, containerization, cloud AI services, and automated deployment.
- Strong knowledge of model evaluation, benchmarking, error analysis, drift detection, explainability, robustness, safety testing, and human-in-the-loop validation.
- Understanding of AI security, privacy, responsible AI, governance, intellectual property, and regulatory controls.
- Ability to design reliable distributed services, APIs, batch or streaming pipelines, observability, capacity models, and production-support processes.
- Proven expert-level track record designing, delivering, and operating production machine-learning or generative-AI systems.
- Experience working with AI assistant platforms and CLI tools.
- Strong scientific reasoning, systems thinking, technical leadership, documentation, communication, and stakeholder-management abilities.
- Professional working proficiency in English.
- Relevant AI, machine-learning, cloud, data, security, or architecture certifications are desirable but not mandatory.
Categories
About NTT DATA
NTT DATA is a Tokyo‑headquartered, publicly traded IT services firm within the NTT Group. It provides consulting, application development, system integration, cloud and enterprise application services, and managed/outsourcing services for large enterprises and public-sector organizations. The company operates in more than 50 countries and is listed on the Tokyo Stock Exchange.