AI ML Engineer I

Key Deliverables

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

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