Sobre este puesto de AI/ML Engineer II en Astreya
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
