Principal Cloud Developer - AI/ML

Position Summary

Key Responsibilities

• Own end-to-end architecture and technical roadmap for data-intensive and AI-enabled enterprise applications.
• Design scalable, secure, resilient cloud-native solutions for application, data, and AI workloads.
• Lead architecture reviews, technical governance, and technology selection.
• Drive engineering best practices including TDD, code reviews, CI/CD, automation, and MLOps/LLMOps practices.
• Architect data platforms, APIs, microservices, distributed systems, and AI service integration patterns.
• Lead the design and operationalization of AI/ML solutions, including model deployment, monitoring, drift detection, and retraining strategies.
• Evaluate and implement Generative AI use cases such as LLM-powered assistants, RAG architectures, prompt orchestration, and agent-based workflows where relevant.
• Establish observability, monitoring, reliability, incident response, and governance practices for both software and AI systems.
• Mentor engineers and influence technical direction across teams.
• Partner with Product, Security, Infrastructure, Data Science, and Business stakeholders.

Required Qualifications

• 12+ years of software engineering experience.
• 5+ years in architecture or technical leadership roles.
• Expertise in programming languages like Java, Python, or Go, Framework like ReactJS, Angular and NodeJS.
• Strong OOAD, design patterns, microservices, and distributed systems experience.
• Strong API design experience using REST, JSON/XML, Swagger, and Postman.
• Experience with PostgreSQL, SQL Server, Oracle, and exposure to NoSQL databases.
• Experience with Linux/Unix, HTTP, caching, scalability, and performance optimization.
• Agile/Scrum, TDD, unit testing, and troubleshooting expertise.
• Strong understanding of AI/ML fundamentals, model lifecycle, feature engineering, and production deployment patterns.
• Hands-on experience with AI/ML frameworks and platforms such as TensorFlow, PyTorch, Scikit-learn, or equivalent.
• Experience building or integrating Generative AI solutions using LLMs, prompt engineering, embeddings, vector databases, and RAG patterns.
• Knowledge of MLOps/LLMOps practices including model versioning, evaluation, monitoring, experimentation, and governance.
• Ability to assess AI solution trade-offs across accuracy, latency, scalability, security, explainability, and cost.
• Effectively communicate product architectures, design proposals, and negotiate options at business unit and executive levels.

Preferred Qualifications

• AWS and/or Azure/GCP cloud platforms.
• Infrastructure as Code (Terraform, CloudFormation).
• Monitoring, Observability, SRE, and Platform Engineering practices.
• Experience with AI platform services such as Amazon Bedrock, SageMaker, Azure OpenAI, Vertex AI, or equivalent.
• Familiarity with vector databases, model serving and inference optimization.
• Knowledge of responsible AI, model governance, privacy, and security considerations for enterprise AI adoption.

Health & Wellbeing

Personal & Professional Development

Unconditional Inclusion

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Job Level

Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.

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