S
Shashwath Solution
AI Operations Engineer
Skills
Google Cloud PlatformData EngineeringDockerPythonJenkinsGrafanaMachine LearningAIAWSPrometheusGitHubKubernetesNo-code AutomationsELK StackApache AirflowPerformance ManagementProcess ImprovementComplianceTerraformAzure
1 month ago
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Important information
Contract type:
Permanent
Location:
Pune, Maharashtra, India
Work mode:
Onsite
Published on:
23 September 2026
What they need
- Key Responsibilities
- AI Model Deployment & Integration:
- Deploy and manage AI/ML models, including traditional machine learning and GenAI solutions (e.g., LLMs, RAG systems).
- Implement automated CI/CD pipelines for seamless deployment and scaling of AI models.
- Ensure efficient model integration into existing enterprise applications and workflows in collaboration with AI Engineers.
- Optimize AI infrastructure for performance and cost efficiency in cloud environments (AWS, Azure, GCP).
- Monitoring & Performance Management:
- Develop and implement monitoring solutions to track model performance, latency, drift, and cost metrics.
- Set up alerts and automated workflows to manage performance degradation and retraining triggers.
- Ensure responsible AI by monitoring for issues such as bias, hallucinations, and security vulnerabilities in GenAI outputs.
- Collaborate with Data Scientists to establish feedback loops for continuous model improvement.
- Automation & MLOps Best Practices:
- Establish scalable MLOps practices to support the continuous deployment and maintenance of AI models.
- Automate model retraining, versioning, and rollback strategies to ensure reliability and compliance.
- Utilize infrastructure-as-code (Terraform, CloudFormation) to manage AI pipelines.
- Security & Compliance:
- Implement security measures to prevent prompt injections, data leakage, and unauthorized model access.
- Work closely with compliance teams to ensure AI solutions adhere to privacy and regulatory standards (HIPAA, GDPR).
- Regularly audit AI pipelines for ethical AI practices and data governance.
- Collaboration & Process Improvement:
- Work closely with AI Engineers, Product Managers, and IT teams to align AI operational processes with business needs.
- Contribute to the development of AI Ops documentation, playbooks, and best practices.
- Continuously evaluate emerging GenAI operational tools and processes to drive innovation.
- Qualifications & Skills
- Education:
- Bachelors or Masters degree in Computer Science, Data Engineering, AI, or a related field.
- Relevant certifications in cloud platforms (AWS, Azure, GCP) or MLOps frameworks are a plus.
- Experience:
- 3+ years of experience in AI/ML operations, MLOps, or DevOps for AI-driven solutions.
- Hands-on experience deploying and managing AI models, including LLMs and GenAI solutions, in production environments.
- Experience working with cloud AI platforms such as Azure AI, AWS SageMaker, or Google Vertex AI.
- Technical Skills:
- Proficiency in MLOps tools and frameworks such as MLflow, Kubeflow, or Airflow.
- Hands-on experience with monitoring tools (Prometheus, Grafana, ELK Stack) for AI performance tracking.
- Experience with containerization and orchestration tools (Docker, Kubernetes) to support AI workloads.
- Familiarity with automation scripting using Python, Bash, or PowerShell.
- Understanding of GenAI-specific operational challenges such as response monitoring, token management, and prompt optimization.
- Knowledge of CI/CD pipelines (Jenkins, GitHub Actions) for AI model deployment.
- Strong understanding of AI security principles, including data privacy and governance considerations.
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