Python Developer

Anaptyss; Dadri; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Anaptyss is seeking a Python Developer with strong CI/CD and MLOps expertise to design, automate, and maintain ML pipelines from experiments to production. You will collaborate with data scientists, ML engineers and DevOps to make model training, deployment, and monitoring repeatable, secure, and scalable.

Responsibilities include building and maintaining CI/CD pipelines for ML workloads, operationalizing end-to-end MLOps workflows, containerizing models with Docker and Kubernetes, and

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• Strong Python skills including OOP, async, packaging, and testing.
• Hands-on experience with ML serving frameworks (FastAPI/Flask).
• Proven CI/CD expertise with Azure DevOps, GitHub Actions, GitLab CI, or Jenkins.
• MLOps experience with MLflow, Kubeflow, Airflow, DVC; Docker/Kubernetes for deployments.
• Experience with one major cloud platform (Azure/AWS/GCP).
• Familiarity with Git workflows, branching, and release management.
• Understanding ML lifecycle: feature engineering, training, deployment patterns.
• Experience with monitoring/observability tools (Prometheus, Grafana, ELK).

Responsibilities

• Design and maintain CI/CD pipelines for applications and ML workloads.
• Build end-to-end MLOps workflows: data validation, training, evaluation, registry, deployment, rollback.
• Containerize and deploy models/services using Docker and Kubernetes.
• Implement model/data versioning, experiment tracking, and reproducibility.
• Set up monitoring for model performance, data drift, system health with alerts and retraining triggers.
• Manage infrastructure using Infrastructure-as-Code (Terraform, Bicep, or CloudFormation).
• Collaborate with data scientists to convert notebooks into robust production pipelines.
• Enforce code quality through reviews, testing, and static analysis; document runbooks.

Skills

Python

CI/CD

MLOps

FastAPI/Flask

Docker

Kubernetes

Git workflows

Terraform

Cloud platforms

Education

Bachelor's or Master's in CS/Engineering

Tools

MLflow

Kubeflow

Airflow

DVC

Job description

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

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,
• 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, 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,

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.
• 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.