Study and transform data science prototypes using appropriate ML and GenAI architectures.
Design and develop machine learning and LLM-powered systems using modern GenAI architectures (e.g., RAG, prompt engineering, embeddings, vector databases).
Solve complex problems with multilayered data sets and optimize existing machine learning libraries and frameworks.
Construct optimized data pipelines to feed both traditional ML models and LLM-based systems.
Run machine learning tests and experiments and document findings and results.
Implement and monitor model and data quality checks to ensure accuracy and consistency of our models and pipelines in production.
Ensure system reliability, performance optimization, and responsible deployment of ML and LLM solutions in production.
Manage the full model lifecycle, including retraining, performance monitoring, and continuous improvement.
Partner with Product, Engineering and Business stakeholders to translate requirements into scalable AI/ML and GenAI solutions, contributing to system architecture and technical design.
Provide technical guidance and support on AI/ML initiatives, delivering clear, actionable insights to stakeholders.
Contribute to AI engineering standards and best practices across the organization.
Create and maintain comprehensive documentation for ML and GenAI systems, including prompt libraries, evaluation frameworks, and architectural decisions.
Establish and promote best practices across MLOps, LLMOps and AI system governance.

Requirements

Master’s degree in Computer Science, Mathematics, or a related field, or equivalent working experience
5+ years of proven experience as a Machine Learning Engineer, with significant experience working with data from structured and unstructured data sources, ETL processes, and data quality management.
Strong proficiency in SQL, Databricks, AWS services, Python, and Spark.
Experience with ML frameworks such as XGBoost, Scikit-learn, TensorFlow, Keras, or PyTorch.
Hands-on experience building and deploying LLM-powered applications, including prompt engineering, evaluation frameworks, Retrieval-Augmented Generation (RAG), embedding models/vector databases.
Familiarity with MLOps and/or LLMOps tooling and CI/CD workflows.
Excellent analytical and problem-solving skills with a strong ability to derive insights from complex data sets.
Effective communication skills with the ability to convey technical information and business impact to non-technical stakeholders.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningGenAI architecturesdata pipelinesSQLDatabricksAWS servicesPythonSparkXGBoostScikit-learn

Soft Skills

analytical skillsproblem-solving skillscommunication skills

Certifications

Master’s degree in Computer ScienceMaster’s degree in Mathematics