solace , containerization , Maven, Web Methods, JUnit, Docker, Shell scripting, Python, Java, Mq, Sql, Devops, Jenkins, Git, Gradle, Linux, Perl, Bitbucket, Ansible, Web Security, Kubernetes, basic scripting, Data model design, Linux shell commands, Run Deck, Messaging concepts, Java 17, Spring Boot 3.x

Microservices, Typescript, Javascript, Docker, Python, AWS, Java, Devops, Gcp, Agile Methodologies, Azure, Kubernetes, AI expertise, vector databases, ML pipelines, prompt context engineering, RAG architectures, LangChain, agentic AI frameworks, fine-tuning, LLMs, generative AI, cloud-native architecture, CI-CD, SaaS multi-tenant products

Java, Apis, Automated Testing, Microservices, Devops, Distributed Systems, Python, Generative AI, LLM platforms, AI-assisted software development platforms, MCP servers, prompt engineering, Google Cloud platforms, agentic workflows, modern software delivery practices, Operational Excellence, cloud-native architectures, AI governance, observability, RAG architectures

Machine Learning, Power Bi, Veeva, Tableau, Sql, Qlik, Python, Salesforce, Data visualization business intelligence tools, Data management analysis platforms, Ai, IQVIA, R, Cloud-based data warehouse analytics platforms, Data science methodologies

Algorithm Development, Scipy, Java, Machine Learning, Deep Learning, Tensorflow, Numpy, Spark, Data Analytics, Python, scikit-learn, MxNet, MLLib, reinforcement learning, R, Time Series Forecasting

operational support , Google Cloud Platform, SLA, Linux, Cloud Architecture, Terraform, Distributed Systems, Incident Management, Microsoft Azure, Kubernetes, AWS, Networking, Infrastructure as Code, GitOps, On-Call operations, Rca, customer onboarding, infrastructure readiness, SLO, go-live governance, Customer Enablement operations, Troubleshooting, operational KPIs

data engineering , Gcp, MLops, Python, Sql, Distributed Computing, Orchestration, Data Governance, Data Lineage, Performance Monitoring, Lakehouse architectures, Automated alerting, MLOps platforms, Observability frameworks, Data quality metrics, Machine learning lifecycle, Data pipelines, CI/CD for machine learning systems