Job Description
Location: Noida
Experience: 4-7 years
Employment Type: Full-time
About the Role
We are looking for a Python Developer with strong CI/CD and MLOps expertise to build,
automate, and maintain the pipelines that take machine learning models from
experimentation to reliable production. You will work closely with data scientists, ML
engineers, and DevOps teams to make model training, deployment, and monitoring
repeatable, secure, and scalable.
Key Responsibilities
Develop clean, testable, production-grade Python code for ML services, APIs,
and data/model pipelines.
Design and maintain CI/CD pipelines for application and ML workloads (build,
test, security scan, deploy).
Build and operationalize end-to-end MLOps workflows: data validation, training,
evaluation, model registry, deployment, and rollback.
Containerize and deploy models and services using Docker and Kubernetes.
Implement model and data versioning, experiment tracking, and reproducibility
practices.
Set up monitoring for model performance, data drift, and system health, with
alerting and automated retraining triggers.
Manage infrastructure using Infrastructure-as-Code (Terraform, Bicep, or
CloudFormation).
Collaborate with data scientists to convert notebooks and prototypes into robust,
modular pipelines.
Enforce code quality standards through code reviews, unit and integration
testing, and static analysis.
Document architectures, pipelines, and runbooks, and support production
incident resolution.
Required Skills & Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, or a related
field.
Strong proficiency in Python, including OOP, async programming, packaging,
and testing (pytest).
Hands-on experience with FastAPI or Flask for serving models and services.
Proven experience building CI/CD pipelines with tools such as Azure DevOps,
GitHub Actions, GitLab CI, or Jenkins.
Solid MLOps experience with tools such as MLflow, Kubeflow, Airflow, DVC,
Azure ML, AWS SageMaker, or Vertex AI.
Working knowledge of Docker and Kubernetes for containerized deployments.
Experience with a major cloud platform (Azure, AWS, or GCP).
Familiarity with Git workflows, branching strategies, and release management.
Understanding of ML lifecycle concepts: feature engineering, training, evaluation,
and deployment patterns (batch, real-time, canary, blue/green).
Experience with monitoring and observability tools (Prometheus, Grafana, ELK,
or Azure Monitor).
Good to Have
Experience with Infrastructure-as-Code (Terraform, Bicep, Helm).
Exposure to feature stores (Feast, Tecton) and data pipelines (Spark,
Databricks).
Knowledge of LLMOps: deploying and monitoring LLM-based applications,
prompt/version management, and RAG pipelines.
Experience with model explainability, bias detection, and governance in regulated
environments (e.g., BFSI).
Understanding of DevSecOps practices: secrets management, SAST/DAST,
dependency scanning.
Relevant certifications (Azure DevOps Engineer, Azure AI Engineer, AWS ML
Specialty, CKA).
Soft Skills
Strong problem-solving and debugging skills.
Clear communication and the ability to work across data science, engineering,
and business teams.
Ownership mindset with attention to reliability and automation.
Ability to mentor junior developers and drive best practices.
What We Offer
Opportunity to work on production AI/ML platforms at scale.
Collaborative, learning-focused environment.
Competitive compensation and benefits
More Info
Key Skills
About Company
