DevOps/MLOps Engineer

Company Description

Cydez Technologies is a prominent IT Digital Transformation and IT Service Management company based in Kochi, Kerala. The organization specializes in delivering innovative digital solutions that help businesses optimize their operations and achieve exceptional efficiency. By leveraging cutting-edge technologies and offering top-notch services, Cydez Technologies crafts tailored IT strategies and robust management solutions. The company is committed to empowering organizations to overcome digital challenges and drive sustainable growth.

The DevOps/MLOps Engineer enables robust, scalable, and secure AI solution delivery by bridging development and operations within the AI Factory. This role focuses on automating, monitoring, and optimising the deployment and lifecycle management of AI/ML systems, ensuring seamless integration with SAP and cloud-native infrastructure.

Skills Required

Education: Bachelor's or Master's in Data or Computer Science or related fields

Advanced coding/scripting (Python, Bash, Shell)

Deep experience with CI/CD tools (Jenkins, GitHub Actions, MLFlow)

Experience with cloud infrastructure (Azure preferred; AWS/GCP beneficial) and SAP BTP integration.

Containerisation (Docker, Kubernetes), infrastructure-as-code (Terraform, Ansible)

Understanding of LLM orchestration and agentic AI frameworks (LangChain, CrewAI, AutoGen) AgentOps

Understanding of MLOps best practices (model, data, concept drift) and their implementation

Commitment to security, compliance, and best practices in operational AI.

Task

CI/CD & Automation: Design, implement, and maintain CI/CD pipelines (e.g., Jenkins, GitHub Actions, Piper, MLFlow) to support rapid, reliable software and model delivery

Cloud & Infrastructure: Deploy, monitor, and manage solutions on cloud platforms (Azure, AWS, GCP, SAP BTP), leveraging containerisation (Docker, Kubernetes) and infrastructure-as-code

Agentic & MLOps: Support agentic AI and LLM orchestration frameworks (LangChain, CrewAI, AutoGen, Haystack), and implement AgentOps/ML Ops for lifecycle management, monitoring, and evaluation of agentic, LLM as well as ML systems (including model, data and concept drift)

Collaboration: Work closely with AI Engineers, Data Scientists, Product Owners, and cross-functional teams to integrate DevOps best practices into all stages of the AI lifecycle

Continuous Improvement: Drive automation, standardisation, and adoption of DevOps culture, fostering collaboration and knowledge sharing across teams