Key skills
PythonAIArtificial IntelligenceMachine LearningMLDeep LearningGenerative AIPyTorchTensorFlowMLOpsAWSAzureGCPKubernetesDockerLeadershipMentoringCommunication
About this role
Janea Systems is a dynamic team of software engineering specialists providing high-impact software development services to Fortune 500 companies. They are seeking a Lead Machine Learning Engineer to lead the design and deployment of scalable machine learning systems, guiding architecture decisions and mentoring engineers.
Responsibilities:
- Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients
- Work at the intersection of machine learning, distributed systems, and production software engineering
- Help transform cutting-edge ML research into robust, production-grade solutions
- Guide ML architecture decisions
- Mentor engineers
- Collaborate with cross-functional teams to deliver high-impact AI solutions across multiple industries
Requirements:
- 8+ years of experience in software engineering, machine learning engineering, or applied AI
- 3+ years of experience in technical leadership or mentoring engineering teams
- Strong experience developing and deploying machine learning models in production environments
- Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or similar
- Experience designing scalable ML systems and data pipelines
- Strong programming skills in Python and experience with backend or distributed systems
- Experience with cloud platforms (AWS, Azure, or GCP)
- Experience with containerization and deployment technologies (Docker, Kubernetes)
- Strong understanding of MLOps practices and model lifecycle management
- Excellent problem-solving skills and ability to work independently in remote environments
- Strong written and spoken English communication skills
- Degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
- Experience working with LLMs, generative AI systems, or deep learning architectures
- Experience building ML platforms or internal AI tooling
- Familiarity with distributed training and large-scale data processing frameworks
- Experience with feature stores, model monitoring, and ML observability tools
- Experience delivering ML solutions in client-facing consulting environments