Data Scientist – ML, GenAI & Agentic AI
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 common
ML 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
