Location: Montreal
Position Overview
The Fusion Machine Learning team is a multi-disciplinary team of engineers and researchers developing AI/ML solutions to some of the biggest problems in 3D design, manufacturing, and mechanical engineering. We are seeking a
Senior AI Developer
who is experienced and passionate about leveraging
LLMOpsand Agent Opspractices to build and scale AI applications integrating LLMs/VLMsandagent-based architectures.
You will collaborate with researchers and engineers to continuously improve the data, training, and release pipelines by automating repositories to ensure quality and interoperability with deployment systems and upgrading prototype code to run on large cloud-based ML training infrastructure.
You will report to the Fusion Platform ML manager and play a critical role in the Autodesk AI strategy.
The team is hybrid-remote,locatedacross Canada and the US.
Responsibilities
AI Application Development
:
Develop and enhance AI applications using pre-trained or existing models (LLMs, VLMs, 3D CAD models) and ensure the seamless integration of AI components into larger software systems
End-to-End System Integration
:
Build and integrate end-to-end systems and applications, focusing on system design, user experience, and application logic
LLMOpsand Agent Ops :
Implement andoptimize
LLMOpsand Agent Opspractices
Automation and Operational Efficiency
:
Identifyopportunities to streamline processes, automate workflows, and improve research and development velocity
Best Practices and Governance
:
Advocate for andestablishbest practices in code quality, infrastructure maintenance, model governance, security, and compliance
Design and Collaboration
:
Participatein design discussions with software architects and researchers, ensuring smooth transitions from research to production. Collaborate with diverse cross-functional teams including product managers and UX designers
Minimum Qualifications
Degree in Data Science, Computer Science, Statistics, or a related field, or equivalent professional experience
Proficient in Python and at least one other widely used programming language (e.g., C++, Java, JavaScript, etc.)
3-5 years of experience inMLOps/ Dev Ops in a production environment
Experience with industry best practices for developing andmaintainingcomplex codebases
Self-starter with the initiative to find solutions and solve problems independently
Comfortable adapting to changing requirements and working in ambiguous areas
Ability to break down large problems into smaller components and provide clear solutions
Preferred Qualifications
8+ years of experience in software development
Agent-Based Frameworks and Tools:
Familiarity with advanced frameworks such asLangFlow,Llama Index,Lang Graph, and agent-based frameworks. Experience with agent/LLM/multi-componentpipeline evaluations
Observability Frameworks:
Experience with observability frameworks like Arize,Comet,Phoenix,Langfuse,MLflow, RAGAS,Dynatraceetc.
Retrieval-Augmented Generation:
Experienceleveragingretrieval-augmented generation (RAG) techniques
Context Engineering:
Experience with context engineering for enhancing AI model performance
Experience building reliable and scalableinference
APIs (e.g., Flask,FastAPI)
Proficiencywith CI/CD pipelines for machine learning projects
Expertisein containerization technologies (e.g., Docker, Kubernetes) for orchestrating and scaling machine learning applications
Experience with AI platforms like Databricks, Sage Maker, Vertex AI, etc.
Experience with cloud data processing, training, deployment, or operations (e.g., AWS, GCP)
Experience developing web applications and APIs
Experience in implementing Infrastructure as Code (IaC) using tools like Terraform
Understanding ofsecurity best practices inMLOps, including data encryption, access controls, and compliance standards
Familiarity with leveraging inference accelerator tools (ONNX,Tensor
RT, Triton) for real-time and high throughput inference runtimesis a plus
Experience with CAD software or in the design and manufacturing industries is a plus
Familiarity with machine learning, deep learning, and statistical modeling tools and libraries (e.g.,PyTorch, Tensor Flow, Pandas,Sci Kit Learn…
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