AI & Data Engineer building production-ready data and AI solutions for Kyndryl. Working with cloud-native platforms and supporting the full lifecycle of AI applications.
Responsibilities
Build and operationalize scalable data and AI solutions by translating architectural designs into production-ready systems.
Develop and maintain data pipelines and AI-enabled services using cloud-native technologies.
Implement and integrate machine learning and LLM-powered solutions within enterprise workflows.
Contribute to real-time and batch data architectures (data lakes, streaming platforms, event-driven systems).
Support MLOps practices, including model versioning, deployment, monitoring, and lifecycle management.
Collaborate with solution architects, data scientists, and software engineers to deliver MVPs and production solutions in fast, iterative cycles.
Build secure, scalable APIs and services for AI and data applications.
Contribute to containerized and orchestrated deployments (Docker, Kubernetes).
Work in agile development environments, managing pull requests, code reviews, and CI/CD pipelines.
Requirements
2–3 years of experience in data engineering, software engineering, or AI engineering roles.
Strong programming skills in Python; experience with C++, C#, or Rust is a plus.
Experience with cloud platforms (AWS, Azure, GCP) and cloud-native development.
Hands-on experience with data platforms (data lakes, data warehouses, streaming systems).
Familiarity with distributed systems, concurrency, and asynchronous programming.
Experience with containerization (Docker) and orchestration concepts.
Knowledge of API design, authentication, and secure execution environments.
Exposure to MLOps practices, model deployment, and monitoring.
Familiarity with LLMs, generative AI, and agentic AI concepts.
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