A/AI Machine Learning Engineering Stf
Posted Updated
Description
Standard Job Description
Responsible for developing, integrating, and deploying artificial intelligence and automation algorithms for data engineering systems, supporting the technology development life cycle from requirements generation through development, integration, and testing. Develops, integrates, and implements algorithms to enable intelligent data pipeline orchestration, automated data quality validation, metadata discovery, anomaly detection, and predictive data operations functionality within data engineering platforms and tooling. Translates requirements from data engineering teams and applies them to development code, integrating AI, machine learning, and automation algorithms into data platforms, ETL/ELT workflows, and data infrastructure. Determines optimal methods to acquire, process, and operationalize knowledge from data systems; implements algorithms to train systems to recognize data patterns, predict pipeline failures, optimize resource allocation, and automate routine data operations tasks. Responsible for various phases of developing and maintaining AI/automation software from requirements generation, software design and development through integration, testing, troubleshooting, debugging, and implementation. Reviews test outcomes, conducts troubleshooting, and debugs issues in production data systems. Develops human-machine interface scenarios for data engineers and analysts, breaking complex data workflows into automated tasks. Documents interface requirements and implements intuitive interfaces that enable data teams to leverage AI capabilities without deep ML expertise.
Basic Qualifications
- Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or related technical field
- Master's degree or PhD in Machine Learning, Artificial Intelligence, or related field preferred
- 10+ years professional experience, 5+ years in production ML
- Expert Python, deep learning frameworks (TensorFlow/PyTorch)
- ML fundamentals, cloud platforms, MLOps experience
- Leadership and communication skills
Desired Skills
- Programming: Expert-level proficiency in Python; strong SQL skills; experience with ML libraries (scikit-learn, pandas, NumPy, SciPy)
- Machine Learning Frameworks: Experience with TensorFlow, PyTorch, or JAX for deep learning; scikit-learn for traditional ML
- ML Fundamentals: Deep understanding of machine learning algorithms (especially anomaly detection, time series, NLP), statistical modeling, and optimization techniques
- Data Engineering Platforms: Hands-on experience with modern data orchestration tools (Airflow, Prefect, Dagster) and data transformation frameworks (dbt)
- Big Data Technologies: Production experience with Spark, distributed computing, and processing large-scale datasets
- Data Warehouses: Experience with cloud data warehouses (Snowflake, BigQuery, Redshift, or Databricks)
- SQL Mastery: Advanced SQL skills including query optimization, window functions, CTEs, and performance tuning
- Cloud Platforms: Production experience with AWS, Azure, or GCP (both ML services and data platform services like S3, GCS, Azure Data Lake)
- Version Control & CI/CD: Proficiency with Git, GitHub/GitLab, and CI/CD pipelines for both ML models and data pipelines
- MLOps: Experience with model deployment, containerization (Docker), orchestration (Kubernetes), and integrating ML into data platforms
Skills
- AI
- Airflow
- Anomaly Detection
- Automation
- AWS
- Azure
- Big Data
- BigQuery
- CI/CD
- Cloud
- Containerization
- Dagster
- Data Engineering
- Data Lake
- Data Pipelines
- Data Quality
- Databricks
- dbt
- Deep Learning
- Distributed Computing
- Docker
- ELT
- ETL
- GCP
- Git
- GitHub
- GitLab
- JAX
- Kubernetes
- Machine Learning
- MLOps
- Model Deployment
- NLP
- NumPy
- pandas
- Prefect
- Python
- PyTorch
- Redshift
- S3
- scikit-learn
- SciPy
- Snowflake
- Spark
- SQL
- Statistics
- TensorFlow
- Time Series
- Version Control