AI/ML Engineer II

Scope:  

• Translate business goals into measurable ML goals (KPIs, acceptance thresholds) in collaboration with PMs and data scientists.

• Lead the translation of ambiguous product needs into clear ML metrics and success criteria.

• 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

• Champion cross-functional collaboration & governance

• Pilot new ML tools/frameworks, leading integration into production where appropriate.

• Architect data strategy, championing reproducibility, traceability, and quality across the ML stack

• Spearhead adoption of emerging ML trends; run strategic POCs and lead production rollouts of state-of-the-art techniques.

• Act as a cross-org ML thought leader—aligning product, infra, legal, and UX on responsible ML.

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

Level 3–5 Additional Responsibilities:

• Lead ML solution design, own production deployments, optimize inference models, drive MLOps practices

• Architect end-to-end solutions for AI-driven services (e.g., IT ticket routing, network anomaly detection), lead AI projects

• Define enterprise AI roadmap, establish AI governance, lead high-impact client engagements, scale AI maturity company-wide

• 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)

• Level 1: 1–2 years in data science/ML roles; hands-on with frameworks like scikit-learn or PyTorch

• Level 2: 1–2 years in data science/ML roles; hands-on with frameworks like scikit-learn or PyTorch

• Level 3: 4–6 years experience in ML/AI implementation and deployment

• Level 4: 7–9 years experience; domain expertise (e.g., IT operations or security AI)

• Level 5: 10+ years in software engineering with AI/ML leadership experience

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 (must), Java/C++ (optional), SQL

• 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.