Description

𝐊𝐞𝐲 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬:

• Development & deploy ML pipelines using SageMaker, Bedrock, Lambda, Step Functions

 • Develop LLM-based RAG solutions with Amazon Bedrock, JumpStart, & custom fine-tuned models

 • Integrate vector databases like Pinecone, Weaviate, Amazon OpenSearch

 • Implement full ML lifecycle with CI/CD, monitoring, and MLOps best practices

 • Ensure observability using CloudWatch, Prometheus, Grafana

 • Collaborate on AI-enabled applications (chatbots, summarizers, embeddings)

 • Ensure compliance, model explainability, and security

𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐒𝐤𝐢𝐥𝐥𝐬:

• 6–12 years of experience in Machine Learning / AI Engineering
• Strong hands-on experience with Python, ML frameworks such as Scikit-learn, TensorFlow, PyTorch, and data libraries like Pandas, NumPy
• Experience in building and deploying ML models on cloud platforms such as AWS.
• Good understanding of MLOps, model deployment, monitoring, and CI/CD for ML pipelines
• Experience with SQL, data engineering concepts, and large-scale data processing
• Domain experience in Healthcare / Life Sciences / Pharma / Clinical / Oncology / Real World Data is highly preferred
• Knowledge of NLP, LLMs, RAG, or Generative AI will be a strong advantage
• 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐢𝐧 𝐀𝐖𝐒: SageMaker (Pipelines, Ground Truth, JumpStart), Amazon Bedrock / Claude / OpenAI

Key Skills

Education

Bachelor’s degree