Software engineer ( distributed, Saas, microservices, API , AI/ML)

Responsibilities

• Analyze large-scale structured and unstructured datasets to identify trends, anomalies, risks, and opportunities in security and AI-powered tools.
• Build, curate, and maintain datasets; ensure data integrity across multiple sources for testing and model development.
• Develop, optimize, and test machine learning models (predictive, generative, NLP) and support MLOps workflows for deployment, monitoring, and integration into IAM systems.
• Partner with data scientists to productionize AI/ML models for risk-based access control, anomaly detection, and identity analytics.
• Design and implement quantitative metrics, dashboards, and visualizations to communicate insights and track key performance indicators (KPIs).
• Monitor log and telemetry data to proactively detect potential harms, threats, and misconfigurations.
• Build and deploy containerized applications using Kubernetes, Docker, Terraform, and modern CI/CD practices.
• Write clean, maintainable, and efficient code in languages such as Python, Go, and Java.
• Collaborate with cross-functional teams to integrate IAM features such as Zero Trust, adaptive authentication, and device attestation.
• Troubleshoot and resolve software, infrastructure, and platform-related issues across diverse environments.

Required Skills/Experience

• Bachelor s degree in Computer Science, Software Engineering, or related field (or equivalent experience).
• 3 8 years of professional software development experience.
• Proficiency in one or more programming languages: Python, Go, Java.
• Experience with distributed systems, SaaS platforms, microservices, and REST/gRPC APIs.
• Familiarity with Kubernetes, Docker, Terraform, and cloud-native architectures.
• Knowledge of software security best practices (e.g., OWASP Top 10, Zero Trust, MFA).
• Strong problem-solving, debugging, and collaboration skills.
• Knowledge and/or experience with MLOps or AI/ML concepts, with willingness to grow further in this area.
• Experience working in a large-scale, enterprise environment.
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Preferred Qualifications

• Exposure to MLOps pipelines (model training, deployment, monitoring).
• Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn) or cloud AI services (AWS SageMaker, Bedrock, Salesforce Cloud AI).
• Experience with IAM, Cybersecurity, or compliance frameworks (NIST, ISO, SOC 2).