Description

You will build and deploy machine learning pipelines and LLM-based solutions on AWS.

This role is on-site.

Responsibilities

• Develop and deploy ML pipelines using SageMaker, Bedrock, Lambda, and Step Functions.
• Build LLM-based RAG solutions with Amazon Bedrock, JumpStart, and custom fine-tuned models.
• Integrate vector databases including Pinecone, Weaviate, and Amazon OpenSearch.
• Implement the full ML lifecycle with CI/CD, monitoring, and MLOps best practices.
• Ensure system observability using CloudWatch, Prometheus, and Grafana.

Required Skills

• 6–12 years of experience in Machine Learning or AI Engineering.
• Strong hands-on experience with Python, Scikit-learn, TensorFlow, and PyTorch.
• Proficiency with data libraries such as Pandas and NumPy.
• Experience building and deploying ML models on AWS (SageMaker, Bedrock, JumpStart).
• Good understanding of MLOps, model deployment, monitoring, and CI/CD for ML pipelines.
• Experience with SQL, data engineering concepts, and large-scale data processing.
• Bachelor’s degree in a relevant field.

Preferred Skills

• Domain experience in Healthcare, Life Sciences, Pharma, Clinical, Oncology, or Real World Data.
• Knowledge of NLP, LLMs, RAG, or Generative AI.

Key Skills

Education

Bachelor’s degree