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