AI/ML Engineer I

Key Deliverables

• 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

Preferred Certifications (All Levels)

• Google Cloud Professional Machine Learning Engineer
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• TensorFlow Developer Certificate
• Databricks Certified Machine Learning Professional
• Kubernetes or Docker certification for MLOps roles

Knowledge, Skills & Abilities (KSAs)

• Machine Learning techniques (regression, classification, clustering)
• Deep Learning architectures (CNNs, RNNs, Transformers, LLMs)
• NLP (tokenization, BERT, prompt engineering)
• Big Data fundamentals (Spark, Hadoop)
• Model interpretability, ethics in AI, bias detection
• Cloud-native AI services (AWS Sagemaker, GCP Vertex AI, Azure ML)
• Data governance, security, and ethical AI practices
• Programming: Python, Apps Script
• 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

Technical Expertise

• Understands basic ML/DL principles
• Codes in Python/Apps Script
• 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)
• Understands data engineering best practices