Senior AI Developer (LLM / VLM / Agent Applications)

Job Requisition ID

25WD93624

Décription du poste / Position Overview

La traduction en anglais se trouve plus bas! / The English translation can be found below!

L'équipe Fusion Machine Learning est une équipe multidisciplinaire composée d'ingénieurs et de chercheurs qui développent des solutions d'IA / ML pour résoudre certains des plus grands problèmes liés à la conception 3D, à la fabrication et à l'ingénierie mécanique. Nous recherchons un développeur IA senior expérimenté et passionné par l'utilisation des pratiques LLMOps et AgentOps pour créer et faire évoluer des applications IA intégrant des architectures LLM / VLM et basées sur des agents.

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, located across 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.

LLMOps and AgentOps : Implement and optimize LLMOps and AgentOps practices.

Automation and Operational Efficiency : Identify opportunities to streamline processes, automate workflows, and improve research and development velocity.

Best Practices and Governance : Advocate for and establish best practices in code quality, infrastructure maintenance, model governance, security, and compliance.

Design and Collaboration : Participate in 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 in MLOps / DevOps in a production environment.

Experience with industry best practices for developing and maintaining complex 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 as LangFlow, LlamaIndex, LangGraph and agent-based frameworks; experience with agent / LLM / multi‑component pipeline evaluations.

Observability frameworks : experience with frameworks like Arize, Comet, Phoenix, Langfuse, MLflow, RAGAS, Dynatrace, etc.

Retrieval‑augmented generation : experience leveraging RAG techniques.

Context engineering : experience with context engineering to enhance AI model performance.

Experience building reliable and scalable inference APIs (e.g., Flask, FastAPI).

Proficiency with CI / CD pipelines for machine learning projects.

Expertise in containerization technologies (e.g., Docker, Kubernetes) for orchestrating and scaling machine learning applications.

Experience with AI platforms like Databricks, SageMaker, Vertex AI, etc.

Experience with cloud data processing, training, deployment, or operations (e.g., AWS, GCP).

Experience developing web applications and APIs.

Experience implementing Infrastructure as Code (IaC) using tools like Terraform.

Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards.

Familiarity with inference accelerator tools (ONNX, TensorRT, Triton) for real‑time and high‑throughput inference runtimes.

Experience with CAD software or in the design and manufacturing industries.

Familiarity with machine learning, deep learning, and statistical modeling tools and libraries (e.g., PyTorch, TensorFlow, Pandas, SciKitLearn, PySpark).

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