Fortra is looking to break the attack chain by developing, enhancing, and maintaining Machine Learning pipelines and data science solutions, including data pipelines, infrastructure, and deployment of machine learning models for both existing and new software products.
Requirements
- At least 7 years of experience in engineering and/or data science roles is required with at least 1 year of senior role experience included.
- Multi-year experience as an ML engineer is desirable.
- Extensive experience developing system architecture and solving engineering problems in at least one major programming language such as Python, Java, or C++.
- Comprehensive MLOps background and expertise in data processing, model training, and deployment of models as microservices.
- Substantial experience integrating applications with cloud technologies such as AWS.
- Proven track record of deploying and benchmarking ML models in production environments.
- Solid experience with Python-based frameworks such as PyTorch, TensorFlow, and scikit-learn is a plus.
Responsibilities
- Lead highly complex ML engineering projects with extensive latitude for independent judgment.
- Design, develop, document, test, and debug software engineering solutions for both customer-facing applications and internal use.
- Deploy, scale, and maintain machine learning models in production environments.
- Develop scalable ML architecture and pipelines.
- Collaborate with data scientists to optimize, test, and evaluate ML models.
- Identify and evaluate new platforms and technologies to enhance our existing systems.
- Serve as the technical expert on the deployment and benchmarking of ML models including LLMs, and generative models.
Other
- The ideal candidate has a strong commitment to excellence, brings a wealth of expertise in ML Engineering, is a creative problem solver, and a team player.
- Provide mentorship and assistance to less experienced team members.
- Collaborate with architects and engineering managers to maintain development roadmaps and prioritize new features.
- Engage with data science and engineering teams to understand requirements and constraints for multiple products.
- Acquire domain expertise in at least one cybersecurity application area.
