Scope

• Translate business goals into measurable ML goals (KPIs, acceptance thresholds) in collaboration with PMs and data scientists.

  
  

• Own the full lifecycle from prototyping (incl. deep learning and GenAI) to deployment and monitoring.

  
  

• Develop and maintain observability dashboards and alerts tied to ML metrics and feature drift.

  
  

• Run and safeguard models in real time

  
  

• Pilot new ML tools/frameworks, leading integration into production where appropriate.

  
  

• Act as a cross-org ML thought leader—aligning product, infra, legal, and UX on responsible ML.

  
  

Key Deliverables by Level

Level 1

AI/ML Engineer I

• Cleaned, annotated, and pre-processed datasets for supervised learning models

  
  

• Simple machine learning models (e.g., logistic regression, decision trees) implemented under guidance

  
  

• Exploratory data analysis reports

  
  

• Jupyter notebooks documenting model experiments

  
  

• Unit-tested ML scripts

  
  

• Essential Duties and Responsibilities (All Levels):

  
  

• Assist in data cleaning, feature engineering, testing basic ML models, write and debug simple scripts

  
  

• Develop ML modules, assist in deployment, support data pipelines, contribute to documentation and unit testing

  
  

• Support data preparation, model training under guidance, debug code, attend knowledge sessions

  
  

• Develop and maintain smaller AI modules (e.g., anomaly detection), assist in deployments, write technical documentation

  
  

• Lead development of scalable ML models, integrate into ITSM systems, ensure compliance and performance metricsArchitect end-to-end AI platforms, oversee cross-domain projects (e.g., NLP for service desk, CV for asset tracking)

  
  

Education and/or Work Experience Requirements

Minimum Requirements

• Bachelor’s degree in Computer Science,Data Science, IT, or a related field.Master’s preferred or equivalent experience for senior levels

  
  

• Level 1: 1–2 years in data science/ML roles; hands-on with frameworks like scikit-learn or PyTorch

  
  

• Programming: Python (must), Java/C++ (optional), SQL, Apps Script, ServiceNow

  
  

• Frameworks: TensorFlow, PyTorch, scikit-learn, HuggingFace

  
  

• Tools: Git, Docker, Kubernetes, Airflow, MLflow,Jupyter, Postman

  
  

• Data pipeline skills: SQL, Pandas, data APIs

  
  

• Deployment: Flask/FastAPI, CI/CD, REST APIs, cloud functions

  
  

• Strong analytical and debugging skills

  
  

• Translate business problems into AI solutions

  
  

• Communicate effectively with technical and non-technical stakeholders

  
  

• Work under Agile or DevOps-based workflows

  
  

• Stay current with research and emerging technologies

  
  

• Rapidly learn new AI concepts and tools

  
  

• Translate business challenges into ML solutions

  
  

• Communicate technical findings to non-technical stakeholders

  
  

• Handle ambiguity and balance research with delivery

  
  

• Collaborate across globally distributed teams 

  
  

Competencies

• Each level, 1 - 5, represents a progression in complexity, autonomy, and responsibility. The higher the level, the more critical thinking, leadership, and expertise are required.

  
  

• Technical Expertise

  
  

• Understands basic ML/DL principles

  
  

• Codes in Python/R

  
  

• Familiarity with AI/ML tools such as Jupyter, scikit-learn, or TensorFlow (basic use)

  
  

• Applies supervised/unsupervised ML methods

  
  

• Proficient in TensorFlow/PyTorch

  
  

• Uses cloud ML services

  
  

• Familiar with ML pipelines

  
  

• Documents technical solutions and contributes to code reviews 

  
  

• Designs and builds production-grade models

  
  

• Uses MLflow, Airflow, CI/CD tools

  
  

• Experience with model deployment and monitoring

  
  

• Owns end-to-end AI/ML solutions including architecture, training, deployment, and monitoring

  
  

• Applies domain knowledge to improve model relevance (e.g., IT ops, cybersecurity) 

  
  

• Drives model optimization at scale

  
  

• Understands data engineering best practices

  
  

• Defines org-wide AI/ML standards

  
  

• Oversees architecture for reusable platforms

  
  

• Directs ML model governance and compliance

  
  

• Evaluates and mitigates risks related to fairness, privacy, and regulatory requirements

  
  

• Problem Solving & Innovation

  
  

• Solves small coding and data cleaning problems

  
  

• Ability to analyze and clean datasets 

  
  

• Identifies root causes in data/model issues

  
  

• Applies ML solutions to scoped problems

  
  

• Effective in debugging and troubleshooting code and data issues

  
  

• Selects and tunes algorithms for real-world impact

  
  

• Innovates within team on novel use cases

  
  

Collaboration & Communication:

• Good communication and team collaboration skills 

  
  

• Shares ideas in meetings

  
  

• Communicates findings clearly to peers

  
  

• Contributes to documentation and demos

  
  

• Collaborates cross-functionally to integrate models into services

  
  

• Explains model behavior to technical and semi-technical audiences

  
  

• Interprets results and presents actionable insights to stakeholders

  
  

• Builds trust with cross-functional teams and leadership