Principal Cloud Developer - AI/ML

hpe

Bengaluru, India 12 Years Exp Posted 14d ago

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.

What you need to bring:

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.