Machine Learning Engineer

• Bachelor’s degree in computer science, Engineering, or a related field.
• 6-10 years of experience in Machine Learning engineering or AI system integration.
• Bash and Unix/Linux command-line toolkit is a must-have.
• Hands-on experience with OpenShift, Docker, Kubernetes.
• Knowledge of cloud platforms (e.g. AWS) is a must-have.
• Exposure to data and network security and compliance in AI systems.
• Knowledge of API integration and microservices architecture.
• Proficiency in Python used both for automation and ML-related tasks
• Knowledge of Workflow Orchestrator, such as Ctrl-M
• Good knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
• Experience with Observability framework, such as Langfuse, Elastic Stack, Grafana, OpenTelemetry.
• Understanding of Generative AI (e.g. prompt engineering, RAG pipelines) and Agentic AI concepts.
• Must have banking Domain experience.

You will work closely with data scientists, architects, and DevOps teams to design, implement, and optimize AI pipelines and infrastructure.

Key Responsibilities:

• Develop and maintain automation scripts using Linux shell scripting, Python, or other relevant tools.
• Ensure seamless deployment and integration between cloud/prem environments (AWS).
• Integrate AI models into production environments using containerized platforms such as OpenShift.
• Implement and maintain network security protocols to safeguard AI systems and data pipelines.
• Collaborate with cross-functional teams to understand AI workflows and translate them into robust engineering solutions.
• Monitor and optimize system performance, reliability, and scalability.
• Support CI/CD processes and infrastructure for AI model deployment and updates.