Work closely with stakeholders and software engineers to identify and design high-impact AI solutions
Data Analysis: Leverage traditional machine learning, NLP techniques, and Large Language Models (LLMs) to analyze notes, transcripts and other unstructured text data
Develop models for tasks such as topic modeling, classification, sentiment analysis, entity extraction, and error detection
Establish robust evaluation frameworks (precision, recall, F1) and conduct iterative error analysis to continuously improve performance and reliability
LLM App and API Development: Design and build LLM-powered applications and services to assist technicians
Implement guardrails, validation layers, and hallucination mitigation strategies
System Design and Implementation: Architect, develop, and deploy scalable AI-powered APIs, applications and automated workflows
MLOPs and Deployment: Implement CI/CD pipelines for deployments
Collaboration and Mentoring: Partner with engineering, operations, and business stakeholders to translate real-world challenges into AI solutions

Requirements

Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative discipline — or a PhD in a relevant field
3+ years of experience applying machine learning and AI in production environments, delivering measurable business impact
Strong foundation in traditional ML and NLP (classification, regression, clustering, topic modeling, sentiment analysis) with experience designing robust evaluation frameworks (precision/recall/F1, experimentation, error analysis)
Practical experience working with LLMs, including prompt engineering, structured outputs, and building Retrieval-Augmented Generation (RAG) systems
Experience designing and implementing agentic AI systems, including multi-step reasoning workflows, tool/API orchestration, memory/state management, and production guardrails
Experience building AI-powered applications and APIs, with an understanding of latency, scalability, cost optimization, and reliability tradeoffs
Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn), and experience developing and consuming REST APIs
Experience building data pipelines (ETL/ELT), preprocessing structured and unstructured data, and strong SQL skills working with large-scale datasets
Experience deploying models using Docker and Kubernetes, with familiarity in CI/CD, cloud platforms (GCP) and production monitoring
Strong ability to translate ambiguous business problems into scalable AI systems and communicate effectively with both technical and non-technical stakeholders.

Benefits

Comprehensive total rewards package highlighting competitive salary and bonus structures
Minimum 3 weeks of vacation
Flexible benefits plan to meet the needs of you and your family
Flexibility to work in-office, virtually or a combination of both, based on the role's requirements
Generous company matched pension and share purchase programs
Opportunity to give back to communities in which we work, live and serve
Career growth and learning & development opportunities to develop your skills

Applicant Tracking System Keywords

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

Hard Skills & Tools

machine learningnatural language processinglarge language modelstopic modelingclassificationsentiment analysisentity extractionerror detectiondata analysisevaluation frameworks

Soft Skills

collaborationmentoringcommunicationproblem-solvingstakeholder engagement

Certifications

Master’s degree in Computer ScienceMaster’s degree in Machine LearningMaster’s degree in Data ScienceMaster’s degree in StatisticsPhD in a relevant field