This role focuses on designing, developing, and deploying AI-powered cloud solutions for enterprise applications. The candidate will work on AI/ML pipelines, cloud-native systems, MLOps processes, and scalable data platforms using AWS, Azure, or GCP technologies. The position also involves supporting digital transformation projects and leading technical architecture initiatives for AI solutions.
Key Responsibilities:
- Design and implement end-to-end AI and ML pipelines using cloud-native technologies
- Build scalable architectures for model training, deployment, monitoring, and governance
- Work closely with data science teams to deploy machine learning models into production
- Lead cloud architecture planning for AI workloads including storage, networking, security, and compute services
- Develop cloud-native applications using Kubernetes, containers, and microservices
- Support secure and cost-effective deployment strategies for AI platforms
- Create and maintain data pipelines using Apache Spark, Kafka, Dataflow, and related technologies
- Ensure data privacy, governance, lineage, and compliance standards across systems
- Implement CI/CD pipelines for AI and ML lifecycle management
- Improve monitoring, logging, model versioning, rollback strategies, and performance tuning processes
- Build RESTful APIs and integration services for AI applications
- Collaborate with business teams, engineering teams, and stakeholders to align solutions with business requirements
- Provide technical leadership, architecture guidance, and mentoring support to development teams
- Prepare technical documentation, architecture diagrams, and deployment standards
- Support cloud infrastructure optimization and scalability improvements
- Monitor production systems and resolve technical or operational issues
- Research new AI technologies and recommend innovative solutions for enterprise projects
- Handle end-to-end ownership of AI cloud architecture initiatives
Required Skills:
- Strong experience with cloud platforms such as AWS, Azure, or GCP
- Hands-on knowledge of AI and ML frameworks including TensorFlow, PyTorch, and Scikit-learn
- Experience with MLOps tools such as MLflow, Kubeflow, Airflow, and CI/CD platforms
- Good understanding of data engineering, streaming systems, and distributed data processing
- Experience working with Apache Spark, Kafka, and cloud-based data pipelines
- Knowledge of Kubernetes, containers, and cloud-native application architecture
- Understanding of IAM, cloud security practices, and compliance standards
- Experience designing RESTful APIs and microservices-based applications
- Strong analytical thinking and problem-solving capabilities
- Ability to build scalable, secure, and high-performance cloud solutions
- Good communication and stakeholder management skills
- Ability to work independently and lead technical initiatives effectively
- Strong documentation and solution design abilities
- Experience handling enterprise-scale AI and cloud projects
Preferred Skills:
- Exposure to enterprise AI transformation and automation projects
- Experience working with serverless cloud technologies
- Familiarity with observability, monitoring, and logging tools
- Knowledge of DevOps and infrastructure automation practices
- Understanding of regulatory compliance frameworks such as GDPR or HIPAA
- Experience with hybrid cloud and multi-cloud environments
- Exposure to advanced AI deployment and governance models
- Knowledge of scalable distributed systems and cloud optimization techniques
Education:
- B.Tech / BCA / MCA / B.Sc. in Computer Science, IT, Data Science, Engineering, or Equivalent Qualification