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
