About the role
Education
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field
- Strong academic foundation in statistics, probability, and machine learning
Core Experience
- 5–8 years of professional experience in Data Science, Machine Learning, or Applied AI
- Hands-on experience building, training, and deploying ML models in production environments
- Strong understanding of data preprocessing, feature engineering, and model evaluation
- Experience working on end-to-end ML workflows under guidance of senior/principal team members
- Ability to translate business and product requirements into data-driven solutions
Machine Learning & MLOps
- Practical experience using Amazon SageMaker for:
- Model training and tuning
- Model deployment and inference
- Experience with commonML algorithms using scikit-learn, TensorFlow, or PyTorch
- Familiarity with MLOps concepts such as:
- Model versioning
- Experiment tracking
- Basic monitoring and retraining workflows
- Working knowledge of Docker and exposure to Kubernetes-based deployments
- Experience collaborating with ML engineers to operationalize models
Generative AI & LLMs
- Hands-on experience developing LLM-powered applications using Amazon Bedrock or similar platforms
- Experience building or contributing to Retrieval-Augmented Generation (RAG) pipelines, including:
- Document ingestion and chunking
- Embedding generation
- Similarity search using vector databases
- Practical knowledge of prompt engineering, prompt tuning, and output evaluation
- Understanding of common LLM failure modes such as hallucinations and grounding issues
- Ability to evaluate LLM responses for accuracy, relevance, and safety
Agentic AI (Growing Expertise)
- Exposure to Agentic AI concepts and frameworks such as AgentCore
- Experience implementing:
- Simple Autonomous agents or workflows
- Multi-step reasoning with predefined tools
- Familiarity with tool-calling, agent orchestration, and workflow automation
- Understanding the importance of:
- Guardrails
- Human-in-the-loop mechanisms
- Logging and observability for agent behavior
Cloud Platform:
- Working knowledge of key AWS services, including:
- S3, Lambda, Redshift, IAM
Professional & Collaboration Skills
- Strong Python programming skills and experience working with APIs
- Ability to work effectively in cross-functional teams (product, engineering, analytics)
- Willingness to learn quickly in a fast-evolving AI landscape
- Comfortable taking technical guidance and implementing feedback
- Clear communication of findings, limitations, and model behavior to non-technical stakeholders
Nice to Have
- Initial exposure to AI governance, compliance, or ethical AI practices
- Experience with monitoring LLM outputs and basic evaluation frameworks
- Certifications in AWS, Machine Learning, or Data Science
- Prior experience in enterprise or cloud-native environments