Open Roles

Key Responsibilities:

• Front-End Development: Design and build responsive, user-friendly interfaces using modern frameworks (e.g., React, Vue, Angular) and standard technologies (HTML5, CSS3).

• Back-End Development: Develop robust server-side logic, RESTful APIs, and microservices using languages such as Node.js, Python, or Java.

• Database Management: Design and optimize database schemas (SQL or NoSQL) to ensure efficient data storage and retrieval.

• Code Quality & Testing: Write clean, maintainable code; conduct code reviews; and implement automated testing (unit, integration, and end-to-end) to ensure stability.

• Deployment: Collaborate with DevOps teams to manage deployment pipelines and troubleshoot production issues in cloud environments.

Key Responsibilities:

• CI/CD Pipeline Management: Design, build, and maintain Continuous Integration and Continuous Deployment (CI/CD) pipelines (e.g., Jenkins, GitLab CI, GitHub Actions) to automate software release processes.

• Infrastructure as Code (IaC): Provision and manage cloud infrastructure (AWS, Azure, GCP) using IaC tools like Terraform, Ansible, or CloudFormation.

• Containerization & Orchestration: Manage containerized applications using Docker and orchestrate them with Kubernetes (K8s) for scalability.

• Monitoring & Logging: Implement monitoring solutions (e.g., Prometheus, Grafana, ELK Stack) to track system performance, uptime, and rapid incident response.

• Security Automation: Integrate security practices into the DevOps pipeline (DevSecOps) to ensure infrastructure is secure by default.

Key Responsibilities:

• Pipeline Architecture: Design, build, and maintain scalable ETL (Extract, Transform, Load) or ELT pipelines to ingest data from various sources.

• Data Warehousing: Manage and optimize data warehouses (e.g., Snowflake, Redshift, BigQuery) and data lakes for performance and cost-efficiency.

• Data Modeling: Create logical and physical data models to organize data for efficient analysis and reporting.

• Data Quality & Governance: Implement checks to ensure data accuracy, consistency, and reliability; document data lineage and dictionaries.

• Optimization: Monitor pipeline performance and optimize complex SQL queries and distributed processing jobs (e.g., Spark, Airflow).

Key Responsibilities:

• Model Development: Design and train machine learning models (supervised, unsupervised, deep learning) using libraries like TensorFlow, PyTorch, or Scikit-learn.

• Feature Engineering: Collaborate with data engineers to select, transform, and extract features from large datasets to improve model accuracy.

• MLOps (Deployment): Deploy models into production environments and maintain the ML lifecycle (training, versioning, monitoring for model drift).

• Algorithm Optimization: Fine-tune hyperparameters and optimize algorithms to ensure low latency and high performance in real-world applications.

• Research: Stay updated on the latest AI trends (e.g., LLMs, Generative AI) and experiment with new techniques to solve specific business problems.