Job Description

We are seeking a highly skilled and experienced

MLOps Engineer

with strong expertise in setting up and managing

CI/CD pipelines

and

Kubernetes

. The ideal candidate should have completed

at least four end-to-end MLOps projects

, demonstrating a deep understanding of productionizing ML models, automating workflows, and managing ML infrastructure at scale.

Key Responsibilities:

Design, implement, and maintain scalable and reliable

CI/CD pipelines

for ML models. Deploy and manage machine learning workloads on

Kubernetes

. Collaborate with data scientists, ML engineers, and DevOps teams to ensure seamless integration and delivery of ML solutions. Monitor, maintain, and troubleshoot deployed models in production. Automate ML workflows using tools like MLflow, Kubeflow, or similar. Implement robust version control and reproducibility strategies for data, models, and experiments. Ensure security, compliance, and best practices are followed in the MLOps lifecycle.

Required Skills & Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Minimum

4+ years

of hands-on experience in

MLOps

. Proven track record of working on

at least 4 MLOps projects

end-to-end. Expertise in

CI/CD tools

(e.g., Jenkins, GitLab CI, ArgoCD). Strong hands-on experience with

Kubernetes

(EKS, AKS, GKE, or self-managed). Experience with ML lifecycle tools (e.g.,

MLflow

,

Kubeflow

,

DVC

). Proficient in scripting and programming (Python, Bash, YAML). Familiar with cloud platforms (AWS, Azure, GCP). Solid understanding of containerization (Docker) and infrastructure as code (Terraform/Helm).

Preferred Qualifications:

Experience with monitoring tools like Prometheus, Grafana, or ELK Stack. Knowledge of model drift detection and continuous training. Familiarity with feature stores and data versioning.
Job Types: Full-time, Permanent

Pay: ?9,407.23 - ?47,593.48 per month

Work Location: In person