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As a Data Engineer you will design, implement, and maintain data pipelines in Google Cloud Platform. You will work closely with Product Owner and Solution Architect to understand business, technical needs and objectives as well as with the Data Engineers to ensure the smooth data feed from source systems to various Destinations.&nbsp;&nbsp;More specifically you will: &nbsp;<ul><li>Work closely with Product Owner, Solution Architect and Data Engineers to develop new flows in and migrate the existing on premises infrastructure to GCP.&nbsp;</li><li>Participate in reviewing appropriate architecture, code and requirements and making recommendations to Business and Product.&nbsp;</li><li>Ensure the quality control of data pipelines and promote best data engineering practices.&nbsp;</li><li>Stay current with industry trends and best practices in data engineering, CI/CD, and apply them to enhance our data infrastructure.&nbsp;</li></ul>Who you are<ul><li>At least 2-3 years of experience in a similar position, designing and developing data flows for data products to public cloud platforms &nbsp;</li><li>Experience with Cloud Technologies and Serverless Computing (GCP)&nbsp;</li><li>Knowledge of batch and data streaming technologies (e.g., Kafka, Dataflow, Kinesis).&nbsp;</li><li>Knowledge of Python or any other Object-Oriented programming language.&nbsp;</li><li>Knowledge of version control systems, particularly Git.&nbsp;</li><li>Experience with orchestration tools, (e.g., Apache Airflow, GCP Composer, Astronomer, Airbyte, Perfect ) for workflow automation and scheduling.&nbsp;</li><li>Experience with CI/CD tools and practices (e.g., GitHub Actions, GitLab CI, Docker, Kubernetes). &nbsp;</li><li>Experience with infrastructure as code tools like Terraform will be highly appreciated&nbsp;</li><li>Experience in enterprise transformation projects will be highly appreciated&nbsp;</li><li>Promoter of cooperation and commitment within a team to achieve goals and deliverables.&nbsp;</li><li>Experience working with Agile methodologies / in Agile teams is strongly preferred.&nbsp;</li><li>Strong communication and collaboration skills.&nbsp;</li><li>Self-motivated with strong time management skills.&nbsp;</li><li>Excellent written and verbal communication skills, in both Greek &amp; English.&nbsp;</li></ul>Not a perfect fit?Worried that you don’t meet all the desired criteria exactly? At Vodafone we are passionate about empowering people and creating a workplace where everyone can thrive, whatever their personal or professional background. If you’re excited about this role but your experience doesn’t align exactly with every part of the job description, we encourage you to still apply as you may be the right candidate for this role or another opportunity.What's in it for you<ul><li>Competitive pay, bonus &amp; remuneration package&nbsp;</li><li>Private Health &amp; Medical Insurance &nbsp;</li><li>Hybrid way of working: a blend of remote and office-based working, including the option to work up to 20 days from abroad&nbsp;</li><li>Remote work equipment to help you create a comfortable, ergonomic workspace at home &nbsp;</li><li>Unlimited access to learning resources and trainings&nbsp;</li><li>Vodafone Parental Leave: 16 weeks of fully paid parental leave to all employees regardless of gender, sexual orientation or length of service &nbsp;</li><li>Spirit of Vodafone Day: one day each quarter dedicated to your personal development&nbsp;</li><li>Extra days off: Vodafone Day, Family Day, Volunteering Day &nbsp;</li><li>Needless to say, we cater for your communication wherever you are&nbsp;</li><li>Office amenities (subject to the office location): such as restaurant, gym and parking&nbsp;</li><li>Special employee offers and discounts</li></ul>

Data Engineer (Athens, Thessaloniki, Heraklion Crete)

The Director, AI Quality Engineering Lead ensures that AI systems across the enterprise are safe, fair, reliable, compliant, and observable from pre‑release through continuous operations. This role designs and operates enterprise evaluation frameworks (quality, bias/fairness, robustness, privacy), leads red‑teaming and guardrail development, encodes policy and regulatory requirements as automated checks, and drives metric reporting and post‑incident learning across AI programs. The lead partners closely with Enterprise Architecture, Compliance, Legal/Privacy, Security, Data Governance, and product/engineering teams to enforce quality and safety gates and enable responsible scale. (Aligned to Corporate Policy and Responsible AI principles.) Reporting into the Head of Applied AI Engineering, you will manage a team of QA Engineers and AI Engineers. This role blends deep technical leadership, product engineering strategy, and direct enablement of enterprise use cases and services. You will partner closely with business, legal and technical stakeholders to design and deliver robust, scalable solutions that demonstrate the value of AI across our enterprise in a safe and compliant way. This role is execution-oriented with strong influence on building AI solutions responsibly.Evaluation and Test Strategy<ul><li>Define and run evaluation frameworks for LLMs and multi-modal systems covering performance, fairness, bias, robustness, safety, and privacy.</li><li>Build automatic + human-in-the-loop evaluation pipelines with reproducible scoring, statistical significance, and error analysis.</li><li>Establish SLOs/SLIs for quality (e.g., grounded accuracy, hallucination rate, privacy leak rate, harmful-content rate).</li></ul>Red-Teaming &amp; Safety Guardrails<ul><li>Design and run red-teaming against prompt injection, jailbreaks, sensitive data leakage, tool-abuse, and retrieval abuse.</li><li>Develop guardrail solutions (classification, content safety, PII/PHI detection, grounding checks, allow/deny lists, rate limiting, output filtering) to mitigate hallucinations, leakage, and compliance violations.</li><li>Maintain attack libraries and scenario banks; simulate adversarial behavior and monitor adaptive attacks over time.</li></ul>Pre‑Release Gating &amp; Continuous Assurance<ul><li>Operate pre‑release evaluation gates with baselines, regression checks, canary/A–B testing, and rollback criteria.</li><li>Run continuous evaluation in production with synthetic &amp; real traffic (shadow/ghost testing) and drift, bias, and toxicity monitoring.</li></ul>Stakeholder Leadership &amp; Culture<ul><li>Coach product teams on responsible AI practices and establish quality ownership norms.</li><li>Build a community of practice across Engineering, Security, Data Governance, and Risk—standardizing test data, benchmarks, and review rituals.</li><li>Mentor engineers building evaluation, red-team, and guardrail capabilities.</li></ul>BASIC QUALIFICATIONS<ul><li>7+ years in ML/AI/Platform/Quality roles, including 3+ years evaluating production systems.</li><li>Advanced degree in Computer Science, Engineering or related discipline.</li><li>Proven design &amp; operation of evaluation harnesses and policy-as-code checks in CI/CD.</li><li>Hands-on Python and expertise with evaluation and safety tooling such as: OpenAI Evals, Ragas/LangSmith, DeepEval, Azure AI Content Safety / OpenAI Moderation, Presidio, Guardrails.ai, Nemo, MLFlow, Grafana, Kibana, Azure monitor</li><li>Cloud &amp; MLOps: Azure/AWS, CI/CD (GitHub Actions/Azure DevOps, feature stores, vector DBs (Azure AI Search, OpenSearch, Pinecone), testing in pipelines.</li><li>Excellent communication with executives and engineers</li><li>Experience leading an engineering team.</li></ul>PREFERRED QUALIFICATIONS<ul><li>Led an AI safety/quality function or enterprise red-team program.</li><li>Prior work with AI based applications, tool use/orchestration, and enterprise data governance.</li><li>Regulatory literacy (e.g., GDPR, EU AI Act readiness), and experience collaborating with Compliance/Risk, Security, and Legal.</li><li>Strong grasp of red-team techniques (prompt/indirect injection, jailbreaks, training data extraction, membership inference, model poisoning, tool abuse).</li><li>Experience with retrieval grounding checks, memory governance, and multi-tenant safety controls.</li></ul>

Director, AI Quality Engineering Lead 4946565

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data engineer (athens, thessaloniki, heraklion crete)

Vodafone

director, ai quality engineering lead 4946565

Pfizer Hellas

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