DevOps /ML Engineer

About KnowDis

Knowdis.ai is an AI-first company specialising in e-commerce applications. We harness the power of machine learning and AI to enhance e-commerce operations, optimize customer experiences, and drive growth. If you are passionate about AI-driven product innovation, this is the perfect opportunity to make a meaningful impact. At KnowDis, we are at the forefront of building innovative technology solutions.

About the Role:

We’re looking for a DevOps / ML Engineer who sits at the crossroads of infrastructure, backend development, and machine learning operations. You won’t be building ML models from scratch—but you’ll need a solid understanding of ML algorithms and pipelines to design, deploy, and maintain the systems that power them. Think of this as an MLOps-flavoured backend role: you’ll build CI/CD pipelines, debug ML pipeline failures, propose automation solutions, and keep our production ML systems running smoothly.

What You'll Do:

- Design, build, and continuously improve CI/CD pipelines for both traditional backend services and ML workloads.

- Debug and resolve issues across ML pipelines—from data ingestion to model serving—working closely with the data science team.

- Develop and maintain Python-based backend services and tooling that support our ML infrastructure.

- Propose and implement MLOps automation solutions: model versioning, experiment tracking, automated retraining, monitoring.

- Manage cloud infrastructure (AWS/GCP/Azure), container orchestration (Docker, Kubernetes), and IaC tools (Terraform, Pulumi).

- Monitor production systems, set up alerting, and ensure high availability of ML-powered features.

- Collaborate with data scientists and backend engineers in an agile environment to ship reliable, scalable systems.

What You'll Need:

- 2–3+ years of experience in DevOps, backend engineering, or MLOps roles.

- Strong Python skills—you can write production-grade backend code, not just scripts.

- Solid understanding of ML algorithms and workflows (training, evaluation, deployment)—enough to debug pipeline issues and have informed conversations with data scientists.

- Hands-on experience designing and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, or similar).

- Experience with containerization (Docker) and orchestration (Kubernetes).

- Familiarity with ML tooling: MLflow, Kubeflow, Airflow, DVC, or equivalent.

- A proactive, ownership-driven mindset: you identify bottlenecks and propose solutions before being asked.

- Comfort with agile workflows and fast iteration cycles—you thrive in environments where priorities shift and quality still matters.

Qualifications:

Selection Process:

• Interested Candidates are mandatorily required to apply through the listing on Zigya. Only applications received through this posting will be evaluated further.
• Shortlisted candidates may be required to appear in an Online Assessment and Screening interview administered by Zigya
• Candidates selected after the Zigya screening rounds will be interviewed by KnowDis