The Senior Machine Learning and Artificial Intelligence Scientist will lead the development and production deployment of advanced AI solutions to deliver measurable business impact. Working with cross-functional teams, you will translate ambiguous business objectives into scalable products, designing and implementing machine learning, generative AI, and multi-agent systems using complex, heterogeneous data. You will build production-grade models for forecasting, NLP, and computer vision while managing the full lifecycle from experimentation to monitoring. The role requires proficiency in Python, SQL, PySpark, and frameworks like PyTorch and TensorFlow. You will utilize Azure, Databricks, AWS, and Google Cloud Platform, leveraging tools such as MLflow, Vector Search, and Kubernetes. Key responsibilities include implementing MLOps and LLMOps practices, ensuring data governance, and establishing evaluation frameworks for safety, latency, and business usefulness.
Translate ambiguous business objectives into well-defined analytical problems with measurable success criteria and deployment strategies.
Design, develop, and deploy production-grade machine learning models for forecasting, NLP, computer vision, and other use cases.
Build and deploy generative AI and multi-agent solutions that coordinate specialized agents, tools, and workflows.
Engineer robust data and feature pipelines using batch, streaming, and event-driven patterns to ensure reproducibility and lineage.
Architect scalable cloud-based AI solutions using Azure, Databricks, AWS, and Google Cloud Platform.
Implement MLOps and LLMOps practices for model versioning, automated testing, drift detection, and cost management.
Establish AI evaluation frameworks to measure factuality, safety, bias, latency, and business usefulness.
Present technical findings and architecture decisions to business and executive stakeholders in clear, decision-oriented language.
What we're looking for
Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related technical field; advanced degree preferred.
5+ years of experience developing and deploying machine learning or artificial intelligence solutions in production environments.
Proven success delivering ML or AI solutions that generated measurable business impact.
Strong experience with the complete machine learning lifecycle, including problem formulation, data preparation, model development, deployment, and monitoring.
Experience developing production systems with Python, SQL, PySpark, and common machine learning frameworks.
Experience designing and deploying cloud-based solutions using Microsoft Azure, Databricks, AWS, or Google Cloud Platform.
Experience with modern generative AI architectures, including LLMs, RAG, embeddings, vector search, and agent orchestration.
Experience designing or deploying multi-agent AI solutions that coordinate multiple agents, tools, workflows, or decision steps.
Machine Learning
Python
SQL
Feature Engineering
Model Deployment
Data Pipelines
Model Monitoring
Drift Detection
Statistics
Probabilistic Methods
Data Quality