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.

Competency

Level 1

Level 2

Level 3

Level 4

Level 5

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)
• Leads development of enterprise-wide AI/ML strategies and platforms
• 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
• Anticipates platform-wide AI needs
• Designs scalable solutions to business-wide problems
• Champions reusability and standardization across teams
• Designs AI architectures integrated into critical systems (e.g., service desks, observability)
• Drives disruptive AI innovation
• Aligns AI/ML initiatives with enterprise transformation goals
• Provides strategic oversight for all AI initiatives and cross-org alignment

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
• Coaches junior team members
• Interprets results and presents actionable insights to stakeholders
• Builds trust with cross-functional teams and leadership
• Acts as primary AI contact for programs
• Engages with external partners/vendors on AI innovation
• Represents AI/ML direction in C-level and client-facing conversations
• Evangelizes a culture of innovation and continuous learning in AI/ML

Project Ownership & Execution

• Supports project tasks under supervision
• Tracks simple work using task tools
• Documents code and data usage
• Delivers discrete ML components
• Manages tasks independently
• Leads projects through design, development, testing, and rollout
• Owns project timeline and quality
• Familiar with advanced ML topics (e.g., transformers, reinforcement learning, LLM fine-tuning)
• Coordinates complex programs and integrations
• Leads cross-functional AI initiatives
• Drives data quality and governance initiatives for reliable model outcomes
• Facilitates cross-functional solutioning between product, IT, and operations
• Oversees multi-team programs
• Owns delivery of strategic AI initiatives across departments
• Defines AI success metrics, compliance frameworks, and model governance structures

Strategic Thinking & Leadership

• Understands team mission
• Adopts best practices
• Takes direction and accepts feedback constructively
• Builds and evaluates supervised/unsupervised models independently
• Provides input on technical direction
• Mentors junior engineers
• Designs scalable models and pipelines for production use
• Defines best practices and technical vision
• Influences product and engineering roadmap
• Balances model performance with business objectives and ethical guidelines
• Sets the AI/ML vision and roadmap aligned with business growth goals
• Establishes AI strategy, ethics, and governance
• Influences external clients and industry engagement