AI Operations Engineer

Shashwath Solution; Dadri; Hybrid; INR 1,500,000 - 2,300,000; Full time

Job summary

Shashwath Solution is seeking an AI Model Deployment & Integration lead to deploy and manage AI/ML models, including GenAI, in production. The role will implement CI/CD pipelines and ensure seamless integration with enterprise applications in collaboration with AI Engineers.

Responsibilities include monitoring performance, latency, drift, and cost, plus advancing MLOps practices and tooling governance across cloud platforms such as AWS, Azure, and GCP.

Qualifications

• 3+ years of experience in AI/ML operations, MLOps, or DevOps for AI-driven solutions.
• Hands-on experience deploying and managing AI models, including GenAI, in production.
• Experience with cloud AI platforms such as Azure AI, AWS SageMaker, or Vertex AI.
• Bachelor's or Master's in CS, Data Eng, AI, or related field; cloud/MLOps certifications are a plus.

Responsibilities

• Deploy and manage AI/ML models, including GenAI solutions, in production.
• Implement automated CI/CD pipelines for AI deployments.
• Ensure integration of models into enterprise apps and workflows with AI Engineers.
• Develop monitoring for performance, latency, drift, and cost metrics.
• Collaborate with Data Scientists to improve models and ops playbooks.
• Establish scalable MLOps practices and tooling governance.

Skills

MLOps

CI/CD

Cloud platforms

Python scripting

Containerization

Monitoring tools

Security & compliance

GenAI operations

Education

Bachelor's or Master's in CS/AI

Tools

MLflow

Kubeflow

Airflow

Docker

Kubernetes

Terraform

CloudFormation

Prometheus

Grafana

ELK Stack

Jenkins

GitHub Actions

Job description

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.

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.