TELECOMMUTE Staff Software Engineer
Apetan Consulting
yesterday
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
SeniorJob location
Remote
Tech stack
API
Artificial Intelligence
Airflow
Amazon Web Services (AWS)
Computer Vision
Azure
Batch Processing
Big Data
Cloud Computing
Code Review
Computer Programming
Databases
Distributed Systems
Hadoop
Python
Machine Learning
Natural Language Processing
Recommender Systems
TensorFlow
Software Engineering
Google Cloud Platform
Data Storage Technologies
PyTorch
Spark
Backend
Build Management
Containerization
Scikit Learn
Kubernetes
Kafka
Machine Learning Operations
REST
Data Pipelines
Docker
Microservices
Job description
We are looking for a Staff Software Engineer to lead the design and development of machine learning-powered applications. This role blends strong software engineering fundamentals with applied machine learning, focusing on building scalable, production-grade ML systems. You will set technical direction, drive architectural decisions, and mentor engineers across teams., * Design and build scalable ML-driven applications and services
- Lead architecture for ML systems, including model serving, data pipelines, and APIs
- Collaborate with data scientists to productionize ML models
- Define best practices for ML engineering, deployment, and monitoring
- Ensure reliability, scalability, and performance of ML systems in production
- Drive technical strategy and influence engineering roadmaps
- Review code, mentor engineers, and elevate team standards
- Work cross-functionally with product, data, and platform teams
Requirements
- Strong programming experience in Python (or similar languages)
- Deep understanding of software engineering principles and system design
- Hands-on experience building and deploying ML models in production
- Experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
- Knowledge of data pipelines and distributed systems
- Experience with REST APIs, microservices, and backend development
- Familiarity with cloud platforms (AWS, Google Cloud Platform, or Azure)
- Strong understanding of databases and data storage systems, * Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow)
- Knowledge of real-time inference and batch processing systems
- Familiarity with big data technologies (Spark, Kafka, Hadoop)
- Experience with containerization (Docker, Kubernetes)
- Background in applied AI domains (NLP, computer vision, recommendation systems), * Mentor senior and mid-level engineers
- Influence cross-team architecture and engineering practices
- Drive adoption of best practices in ML lifecycle management, * Strong leadership and decision-making ability
- Excellent communication and stakeholder management
- Ability to operate in ambiguity and drive outcomes
