Who We Are
At NTT DATA, we are a global team of over 140,000 professionals, operating in more than 50 countries and serving diverse industries including telecommunications, finance, public sector, healthcare, and more. We are passionate about technology and innovation, and we believe in personalized, merit-based career growth.
We are expanding our capabilities in Conversational AI, building intelligent assistants across voice and text channels, web, mobile apps, WhatsApp, and Microsoft Teams, using cutting-edge technologies like Generative AI (GPT) and platforms such as Dialogflow CX, BotFramework Composer, Watson Assistant, and Cognigy. If you’re ready to take the next step in your career, we’d love to hear from you. We offer flexibility to suit your needs — whether you prefer working as a freelancer or on a full-time employment contract. The role is fully remote, giving you the freedom to work from anywhere while collaborating with an experienced international team. Hit the apply button and let’s connect!
What You'll Be Doing

• Designing, training, and fine-tuning machine learning and deep learning models.
• Developing LLM-based solutions (RAG pipelines, chatbots, Q&A assistants, classification/extraction models).
• Implementing MLOps pipelines for model deployment, monitoring, and CI/CD across cloud environments.
• Collaborating with data engineers and architects to build scalable data and model workflows.
• Communicating insights and technical decisions to both business and technical stakeholders.
• Preparing documentation and maintaining clear, structured model repositories and experiment tracking.
• Staying current with advancements in AI/ML, LLMs, and emerging applied AI techniques.
  

What You'll Bring Along

• Bachelor’s degree in Computer Science, Information Technology, or a related field (preferred).
• Minimum 5 years of experience developing data pipelines in cloud environments.
• Practical experience with Databricks (Spark / PySpark / Scala).
• Strong SQL skills and experience with ETL / ELT workflows, data modeling, and DWH concepts.
• Hands-on experience with at least one cloud data platform: Azure Data Lake / Synapse, AWS Glue / Redshift, or GCP BigQuery.
• Familiarity with DataOps / DevOps practices: CI/CD (GitLab CI, GitHub Actions, Azure DevOps).
• Knowledge of Docker and basic Kubernetes deployment.
• Experience working with workflow orchestration tools (Airflow, Prefect, or similar).
• Ability to maintain metadata, lineage, and data quality standards.
• Strong collaborative communication skills, comfortable working with Data Scientists, Architects, Product, and Delivery teams.
• Excellent command of written and spoken English.
  

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