Senior Machine Learning Engineer

Posted 13 days agoViewed

💎 Seniority level: Senior, 5+ years

📍 Location: Worldwide

💸 Salary: 169000.0 - 207000.0 USD per year

🔍 Industry: Software Development

🏢 Company: Invisible Technologies👥 101-250💰 Seed almost 4 years agoInformation Services Project Management Information Technology

🗣️ Languages: English

⏳ Experience: 5+ years

🪄 Skills: AWSBackend DevelopmentDockerPythonSQLCloud ComputingFull Stack DevelopmentGitKubernetesMachine LearningNosqlCI/CD

Requirements:

5+ years of software engineering experience, with a strong focus on ML engineering and deploying machine learning models in production.
Extensive experience in full-stack development, particularly in backend environments that support AI/ML workloads.
Strong proficiency in Python, with deep expertise in LLMs, AI Agents, and ML model development.
Experience designing and deploying scalable ML systems, such as retrieval-augmented generation (RAG) pipelines and production-grade AI applications.
Extensive experience with cloud platforms (AWS, GCP, Azure) and operational best practices for ML workloads.
Familiarity with Kubernetes and other container management tools.
Ability to write well-structured, organized code and automated unit/E2E tests.
Comfortable with polyglot persistence models (SQL vs. NoSQL).
Experience with MLOps frameworks and best practices; familiarity with DevOps principles as applied to machine learning models, including model versioning, monitoring, and lifecycle management.
Ability to operate independently in unstructured environments, demonstrating a proactive and investigative approach to tackling challenges
Excellent communication skills, with the ability to collaborate effectively in dynamic, cross-functional teams, including data scientists, researchers, and software engineers.

Responsibilities:

Develop and Maintain AI/ML Systems: Build robust, scalable backend systems that support machine learning operations and data processing pipelines
Cloud Operations and Management: Oversee and optimize cloud infrastructure to ensure efficient deployment and operation of ML models
Problem Solving: Independently explore and address complex problem spaces to improve system capabilities and performance without extensive guidance
Cross-Functional Collaboration: Work closely with ML engineers and data scientists to integrate advanced ML technologies, ensuring seamless operations across various platforms
Innovation and R&D: Actively participate in research and development of new tools that can enhance our AI capabilities and workflows

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