AI/ML Engineer I

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

Title

Key Deliverables

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