About Company
A well-established IT services and digital transformation company with over two decades of industry presence is expanding its team. The organization delivers enterprise software solutions, cloud platforms, AI-driven applications, and secure technology services to global clients across multiple domains. With a strong technical workforce and globally recognized quality and security standards, the company offers a stable environment focused on long-term growth, innovation, and high delivery excellence.
Technical Skills:
Programming: Python, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, MCP Servers.
Version Control: Git, GitHub, Bitbucket.
Databases: SQL, NoSQL, PostgreSQL, MongoDB, Cosmos Db
CI/CD: Jenkins, GitLab CI, CircleCI, Travis CI.
Containerization: Docker, Kubernetes, OpenShift.
Microservices: RESTful APIs, Flask, Django, FastAPI.
Monitoring: Prometheus, Grafana, ELK stack, Splunk
Security: OWASP, SSL/TLS, IAM.
Agile Tools: Jira, Trello, Confluence.
Testing: PyTest, UnitTest, Selenium.
Cloud: GCP, Azure, AWS
Responsibilities:
Additional Responsibilities:
AI Integration: Seamlessly integrate AI models into applications using TensorFlow and PyTorch.
API Development: Develop and maintain APIs using Flask and Django.
Data Pipelines: Build and maintain efficient data pipelines using Pandas and NumPy.
Validation: Ensure data quality and consistency using PyTest and UnitTest.
Scalability: Design systems for optimal performance using Kubernetes and Terraform.
Automation: Automate deployment processes using Jenkins and GitLab CI.
Monitoring: Set up monitoring and logging using Prometheus, Grafana, and ELK stack.
Security: Implement security best practices using OWASP guidelines and IAM.
Testing: Implement automated testing frameworks using Selenium and PyTest.
Feedback: Incorporate user feedback for continuous improvement using agile methodologies.
Requirements:
Education: Bachelor's or master’s in computer science or related field.
Experience: 3-6 years in software development and pipeline engineering.
Technical Proficiency: Strong Python skills; experience with TensorFlow, PyTorch, Keras.
Cloud Platforms: Familiarity with AWS, Google Cloud, Azure.
Deployment: Experience with Docker, Kubernetes, CI/CD tools (Jenkins, GitLab CI, CircleCI).