We are seeking an experienced and talented AI QE Engineer to join our team. In this role, you will be responsible for validating cutting-edge artificial intelligence solutions across various domains, including generative AI, conversational AI, and predictive AI.
The application, hosted in AWS, includes EC2, S3, Lambda, Athena, DymanoDB, OpenSearch, CloudWatch, GLUE, Bedrock, SageMaker, Kendra, Amazon Q, Claude from Anthropic Titan Embeddings from in AWS, Python, Langchain, and Streamlit technologies.
Duties & Responsibilities
- Experience in testing and validating AI/ML solutions including Generative AI, Conversational AI, and Predictive models
- Strong understanding of how ML models are built end-to-end (data preparation, feature engineering, training, validation, tuning)
- Knowledge of core ML algorithms and model types (regression, classification, clustering, tree-based models, neural networks, transformers)
- Proficiency in Python for AI test automation, data analysis, and model output validation
- Hands-on experience with pandas, NumPy, and scikit-learn for data and model validation
- Experience in data quality analysis, profiling, and feature validation
- Understanding of model evaluation metrics and validation of performance results
- Ability to interpret model behavior and explainability outputs
- Experience testing AI APIs and services built using FastAPI or Flask
- Familiarity with cloud-based AI deployments, preferably AWS SageMaker
- Understanding of production ML lifecycle, including deployment validation and monitoring
- Strong analytical, problem-solving, and communication skills for cross-functional collaboration
Desirable
- Hands-on experience testing Generative AI prompts, hallucinations, and response quality
- Familiarity with Responsible AI, bias, fairness, and safety validation
- Knowledge of MLOps pipelines and CI/CD validation for ML systems Experience with performance, latency, and scalability testing for AI services
- Exposure to adversarial testing and edge-case validation for AI models
- Experience testing AI systems in regulated or high-risk domains
Skills Required
- Bachelor's degree (B.E.) from four-year college or university, or equivalent combination of education and experience.
- 9+ years of experience in deploying and testing AI solutions, particularly in the areas of generative AI, conversational AI, and predictive AI.
- Strong proficiency in Python and experience with AI/ML libraries such as PyTorch, NumPy, scikit-learn, TensorFlow, and Keras
- Familiarity with LangChain and other NLP frameworks for building conversational agents and language models
