Data Scientist (AWS Bedrock)

We are seeking a highly skilled Data Scientist with hands-on experience in AWS Bedrock to design, build, and scale generative AI and machine learning solutions. The ideal candidate has a strong foundation in Python, applied ML, LLMs, and cloud-native development with a focus on production-grade models and prompt engineering.

• Develop, fine-tune, and deploy generative AI/LLM solutions using AWS Bedrock, including foundation models such as Claude, Llama, and Titan.
• Build end-to-end ML workflows leveraging AWS services (S3, Lambda, SageMaker, Step Functions, API Gateway, DynamoDB, RDS, etc.).
• Design and implement prompt engineering strategies, evaluation frameworks, and model optimization techniques.
• Integrate Bedrock-powered AI capabilities into applications via APIs and SDKs.
• Collaborate with cross-functional teams to identify business problems and translate them into scalable AI/ML solutions.
• Perform data preprocessing, feature engineering, statistical modeling, and experimentation.
• Develop scalable pipelines for model training, inference, and monitoring.
• Conduct A/B testing, model performance evaluations, and continuous improvement activities.
• Ensure adherence to security, compliance, and responsible AI best practices within AWS.
• Produce clear technical documentation, reports, and model explainability outputs.

• Bachelor's or Master's in Computer Science, Data Science, Engineering, Mathematics, or related field.
• 3 7 years of experience as a Data Scientist or ML Engineer.
• Strong hands-on expertise with AWS Bedrock, including provisioning, model selection, and orchestration.
• Advanced proficiency in Python, including libraries such as NumPy, pandas, scikit-learn, PyTorch or TensorFlow.
• Experience building and deploying ML/LLM applications in AWS.
• Knowledge of vector databases (e.g., Pinecone, FAISS, OpenSearch) and RAG pipelines.
• Strong grasp of data modeling, statistics, NLP, and machine learning algorithms.
• Familiarity with CI/CD, MLOps, containerization (Docker), and version control (Git).
• Strong problem-solving abilities, analytical mindset, and communication skills.

• Experience fine-tuning LLMs using SageMaker or custom training pipelines.
• Prior work with multimodal models, retrieval-augmented generation (RAG), or agent-based architectures.
• Certification: AWS Solutions Architect, AWS Machine Learning Specialty, or equivalent.
• Experience integrating Bedrock with real-time applications and microservices.