Senior AI Developer (LLM/VLM/Agent Applications)

The Fusion Machine Learning team at Autodesk is seeking a Senior AI Developer to build and scale AI applications integrating LLMs/VLMs and agent-based architectures. This role involves leveraging LLMOps and AgentOps practices, collaborating with researchers and engineers to improve data, training, and release pipelines, and upgrading prototype code for cloud-based ML training infrastructure. The developer will play a critical role in Autodesk's AI strategy, focusing on end-to-end system integration and operational efficiency.

Must Have

• Develop and enhance AI applications using pre-trained or existing models (LLMs, VLMs, 3D CAD models).
• Ensure seamless integration of AI components into larger software systems.
• Build and integrate end-to-end systems, focusing on system design, user experience, and application logic.
• Implement and optimize LLMOps and AgentOps practices.
• Identify opportunities to streamline processes, automate workflows, and improve R&D velocity.
• Promote and establish best practices in code quality, infrastructure maintenance, model governance, security, and compliance.
• Participate in design discussions with software architects and researchers.
• Collaborate with diverse cross-functional teams including product managers and UX designers.
• Proficient in Python and at least one other widely used programming language (e.g., C++, Java, JavaScript).
• 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.

Good to Have

• 8+ years of experience in software development.
• Familiarity with advanced agent-based frameworks such as LangFlow, LlamaIndex, LangGraph.
• Experience with agent/LLM/multi-component pipeline evaluations.
• Experience with observability frameworks like Arize, Comet, Phoenix, Langfuse, MLflow, RAGAS, Dynatrace.
• Experience leveraging retrieval-augmented generation (RAG) techniques.
• Experience with context engineering for enhancing 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).
• Experience with AI platforms such as Databricks, SageMaker, Vertex AI.
• 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 of security best practices in MLOps.
• Familiarity with leveraging inference accelerator tools (ONNX, TensorRT, Triton).
• 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).

Perks & Benefits

• Comprehensive benefits package

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 LLMOps and AgentOps practices to build and scale AI applications integrating LLMs/VLMs and agent-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, 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 observability frameworks like Arize, Comet, Phoenix, Langfuse, MLflow, RAGAS, Dynatrace etc.
• Retrieval-Augmented Generation: Experience leveraging retrieval-augmented generation (RAG) techniques
• Context Engineering: Experience with context engineering for enhancing 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 in 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 leveraging inference accelerator tools (ONNX, TensorRT, Triton) for real-time and high throughput inference runtimes is 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, TensorFlow, Pandas, SciKitLearn, PySpark)