Staff Software Engineer, Applied AI (Forward Deployed)
Builds scalable backend systems and deploys ML models in production for client engagements, working embedded with top clients 3-4 days/week in New York. Requires 8+ years experience in ML engineering, Python, LLMs, cloud platforms, and client-facing work.
About the job
What You’ll Do
- 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.
- Client Engagement: Collaborate directly with Invisible’s clients, working embedded with client teams to support use case discovery, product development, and AI deployment.
- Innovation and R&D: Actively participate in research and development of new tools that can enhance our AI capabilities and workflows.
What We Need
- 8+ 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.
- Prior experience working directly with clients in use case discovery, product development, and leading client engagements.
- Technical Expertise: 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).
- ML Operations: Experience with MLOps frameworks and best practices; familiarity with DevOps principles as applied to machine learning models, including model versioning, monitoring, and lifecycle management.
- Problem Solving: Ability to operate independently in unstructured environments, demonstrating a proactive and investigative approach to tackling challenges.
- Communication: Excellent communication skills, with the ability to collaborate effectively in dynamic, cross-functional teams, including data scientists, researchers, and software engineers.
Skills
Python, LLMs, AI Agents, RAG, MLOps, Kubernetes, AWS, GCP, Azure, DevOps
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