Key Responsibilities:

This ML Ops / Integration Engineer role focuses on building and integrating Generative AI and Agentic AI solutions into enterprise environments. You will work closely with data scientists, architects, and DevOps teams to design, implement, and optimize AI pipelines and infrastructure.
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.

Skills Required

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.

Machine Learning Engineer