Job Summary:
We are seeking a skilled
ML Ops / AI Integration Engineer to build, integrate, and manage
Generative AI and Agentic AI solutions within enterprise environments. The ideal candidate will work closely with
data scientists, architects, and DevOps teams to design, deploy, and optimize AI pipelines, ensuring scalable, secure, and reliable AI systems. This role focuses on automation, cloud integration, and production deployment of AI models.
Key Responsibilities
- Develop and maintain automation scripts using Linux shell scripting, Python, and other automation tools.
- Ensure seamless deployment and integration between cloud and on-premise environments (AWS).
- Integrate AI and machine learning models into production environments using containerized platforms such as OpenShift.
- Implement and maintain network security protocols to protect AI systems and data pipelines.
- Collaborate with data scientists, DevOps engineers, and architects to translate AI workflows into scalable engineering solutions.
- Monitor, maintain, and optimize system performance, reliability, and scalability.
- Support and maintain CI/CD pipelines for AI model deployment and continuous updates.
- Implement logging, monitoring, and observability frameworks to ensure system stability.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field.
- 4+ years of experience in Machine Learning Engineering, MLOps, or AI system integration.
- Strong experience with Bash and Unix/Linux command-line tools.
- Hands-on experience with OpenShift, Docker, and Kubernetes.
- Experience with cloud platforms such as AWS.
- Knowledge of network security and compliance practices related to AI systems.
- Experience with API integrations and microservices architecture.
- Proficiency in Python for automation and machine learning related tasks.
- Experience with workflow orchestration tools such as Ctrl-M.
- Good knowledge of logging and monitoring tools such as Splunk and Geneos.
- Experience with observability frameworks such as Langfuse, Elastic Stack, Grafana, and OpenTelemetry.
- Understanding of Generative AI concepts such as prompt engineering, RAG pipelines, and Agentic AI frameworks
