AI Engineer

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