MLOps/DevOps/CloudOps

Position Overview:

• Design and implement machine learning pipelines and LLM-driven agents RAG Agentic AI utilizing AWS Bedrock, SageMaker, and LangGraph.
• Develop, train, and deploy machine learning models for churn prediction and forecasting, adhering to MLOps best practices.
• Create RAG architectures that integrate LLM APIs, focus on prompt engineering, and establish responsible AI guardrails.
• Proficient in Python, Scikit-learn, TensorFlow, PyTorch, and hands-on experience with SageMaker, vector databases such as FAISS, Pinecone, and OpenSearch.
• Required skills include Python, LangGraph, LangChain, Bedrock, and OpenAI integration. Preferred skills include LLM fine-tuning, LangSmith, and AWS ML Specialty.

Core Responsibilities:

• Design, construct, and sustain scalable MLOps and DevOps solutions within the AWS environment.
• Develop and oversee CI/CD pipelines for the deployment of machine learning models and application releases.
• Automate infrastructure provisioning through Infrastructure as Code (IaC) tools.
• Deploy, monitor, and enhance machine learning models in production settings.
• Manage containerized applications utilizing Docker and Kubernetes.
• Implement frameworks for model versioning, experiment tracking, and monitoring.
• Maintain security, reliability, and high availability of the cloud infrastructure.
• Work collaboratively with Data Scientists, ML Engineers, and Development teams to operationalize machine learning workflows.
• Diagnose and resolve issues related to deployment performance and infrastructure.

Technical Qualifications:

• Extensive experience with AWS Cloud Services including EC2, S3, Lambda, IAM, EKS, ECS, CloudWatch, and SageMaker.
• Hands-on experience with MLOps tools like MLflow, Kubeflow, SageMaker, or Vertex AI.
• Familiarity with DevOps tools such as Jenkins, GitHub Actions, GitLab CI/CD, and Azure DevOps.
• Proficient in Python scripting and automation tasks.
• Expertise in Docker and Kubernetes for container management.
• Knowledge of Infrastructure as Code solutions like Terraform or CloudFormation.
• Experience with monitoring tools, including CloudWatch, Prometheus, and Grafana.
• Strong understanding of Linux operating systems and networking principles.
• Proficient with Git for version control.

Desired Skills:

Technology->AI-Data science->PYTHON, Technology->AI-AI Engineering->MLOps, Technology->Cloud Platform->Amazon Web Services, DevOps->Amazon CodeDeploy