Job Description

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