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AI/ML Engineer - MLOps & Cloud Deployment

Jigyasa Analytics โ€ข India
Remote
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AI Summary

Jigyasa Analytics seeks an AI/ML Engineer to deploy and operationalize machine learning solutions in cloud environments. The role involves building data pipelines, containerizing models with Docker, and collaborating with data scientists to transition models from development to production. Candidates must have 1-2 years of experience with Python, cloud platforms, and workflow orchestration tools.

Key Highlights
Deploy ML/AI models to AWS, Azure, and GCP cloud environments
Build and automate data pipelines using Airflow, Windmill, or similar orchestration tools
Containerize applications and models with Docker for production deployment
Collaborate with data scientists to transition models from development to production
Key Responsibilities
Deploy machine learning and AI models to cloud environments (AWS, Azure, GCP)
Build, maintain, and automate data pipelines and model workflows
Develop and manage process-flow automation using tools such as Airflow, Windmill, or similar orchestration platforms
Containerize applications and models using Docker
Collaborate with data scientists to transition models from development to production
Monitor and maintain deployed solutions to ensure reliability and performance
Contribute to MLOps and deployment best practices
Technical Skills Required
Python Cloud platforms (AWS, Azure, GCP) Docker
Benefits & Perks
Fully remote position based in India
Compensation commensurate with experience and qualifications
Nice to Have
Kubernetes or other container orchestration technologies
CI/CD pipelines
Generative AI, LLM applications, or RAG architectures
Model monitoring and version control tools

Job Description


Jigyasa Analytics is looking for an AI / Machine Learning Engineer with 1โ€“2 years of experience to support the deployment and operationalization of machine learning and AI solutions.

Responsibilities
  • Deploy machine learning and AI models to cloud environments (AWS, Azure, GCP).
  • Build, maintain, and automate data pipelines and model workflows.
  • Develop and manage process-flow automation using tools such as Airflow, Windmill, or similar orchestration platforms.
  • Containerize applications and models using Docker.
  • Collaborate with data scientists to transition models from development to production.
  • Monitor and maintain deployed solutions to ensure reliability and performance.
  • Contribute to MLOps and deployment best practices.
Qualifications
  • 1โ€“2 years of professional experience in Machine Learning Engineering, Data Engineering, MLOps, or a related field.
  • Proficiency in Python.
  • Experience working with cloud platforms (AWS, Azure, or GCP).
  • Hands-on experience with Docker and containerized deployments.
  • Experience building and managing data pipelines.
  • Familiarity with workflow orchestration and automation tools such as Airflow, Windmill, Prefect, Dagster, or similar platforms.
  • Understanding of APIs, deployment processes, and software engineering best practices.
  • Strong problem-solving and communication skills.
Preferred Qualifications
  • Experience with Kubernetes or other container orchestration technologies.
  • Familiarity with CI/CD pipelines.
  • Exposure to Generative AI, LLM applications, or RAG architectures.
  • Experience with model monitoring and version control tools.
Additional Information
  • This is a fully remote position based in India.
  • We welcome applications from professionals returning to the workforce after a career break or sabbatical.
  • Compensation will be commensurate with experience and qualifications.


  • #Hiring #MachineLearning #AI #MLOps #DataEngineering #Airflow #Windmill #Python #Docker #RemoteWork #IndiaJobs #GenerativeAI

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