Description

You will lead the end-to-end development, deployment, and optimization of machine learning models within AWS environments.

This role is on-site.

Responsibilities

• Develop and deploy machine learning models using AWS SageMaker and other cloud services.
• Build and maintain MLOps pipelines for model versioning, orchestration, and drift detection.
• Design and optimize APIs for model serving using FastAPI, Flask, or API Gateway.
• Implement generative AI solutions using LLMs, LangChain, and vector databases.
• Manage infrastructure using Terraform, CDK, and containerization tools.

Required Skills

• 5+ years of experience in machine learning and MLOps.
• Expertise in Python with PyTorch or TensorFlow.
• Hands-on experience with AWS services including SageMaker, ECR, Lambda, CloudFormation, IAM, and S3.
• Proficiency with LLMs (GPT, Claude, Llama) and LangChain.
• Experience with vector databases such as Pinecone or FAISS.
• Knowledge of MLOps tools like MLflow, TFX, Kubeflow, or Evidently.
• Competency in API design, caching, async processing, and load balancing.
• Experience with Docker, Kubernetes, and Infrastructure-as-Code (Terraform, CDK).
• Ability to implement monitoring using CloudWatch and SageMaker Model Monitor.

Key Skills

Education

Any Gradute