Responsibilities
- Own AI applications and reusable services through prototyping, integration, production deployment, monitoring, and support.
- Build agents, multimodal applications, LLM workflows, and RAG pipelines using prompt design, tool calling, structured outputs, embeddings, and hybrid search.
- Create ingestion and parsing pipelines that turn datasheets, schematics, test logs, media, and business data into grounded AI capabilities.
- Integrate AI with engineering tools, manufacturing and test systems, and business applications through APIs and MCP servers with validation, security, and human oversight.
- Apply classical ML for classification, anomaly detection, computer vision, and audio processing, and deploy and monitor these models alongside LLM systems.
- Develop repeatable evaluations to improve accuracy, reliability, latency, and cost.
- Develop and operate Python services on Azure with PostgreSQL, Terraform, containers, CI/CD, and observability.
- Shape technical direction, propose architectures and reusable patterns, evaluate models and tooling, collaborate with stakeholders, and mentor engineers through code and design reviews.
Requirements
- At least 5 years of software engineering experience, including delivering and supporting production LLM applications beyond proof of concept.
- Strong Python and backend skills, including API design, testing, debugging, and maintainable software development.
- Hands-on experience with LLM APIs, prompt design, and tool-enabled agents such as LangChain or LangGraph.
- Practical RAG experience with embeddings, vector and hybrid search, reranking, and retrieval and answer quality measurement.
- Solid ML fundamentals covering data processing, feature engineering, model training and evaluation, and production deployment of non-LLM models using PyTorch, scikit-learn, NumPy, and pandas.
- Experience deploying cloud applications with containers, CI/CD, infrastructure as code, and monitoring, preferably on Azure.
- Strong SQL skills, preferably PostgreSQL, plus understanding of authentication, access control, and secure data handling.
- Ability to communicate technical trade-offs to technical and non-technical audiences, work independently, and mentor others.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent experience.
- Preferred experience includes AI or ML for manufacturing, hardware design, automated inspection or testing, audio/video processing, multimodal models, computer vision, anomaly detection, audio and signal processing, MCP server development, LLM observability tools, model serving, fine-tuning open-weight models, and helping non-technical teams adopt AI tools.
Tech Stack
Categories
About Evertz
Evertz Microsystems (TSX:ET) is a leading global manufacturer of broadcast equipment and solutions that deliver content to television sets, on-demand services, WebTV, IPTV, and mobile devices (like phones and tablets). Evertz has expertise in delivering complete end-to-end broadcast solutions for all aspects of broadcast production including content creation, content distribution and content delivery. Considered as an innovator by their customers, Evertz delivers cutting edge solutions that are unmatched in the industry in both hardware and software. Evertz delivers products and solutions that can be found in major broadcast facilities on every continent. Evertz’ customer base also includes telecommunications, satellite, cable TV, IPTV providers, and professional audio-visual, government, military, enterprise, and new media sectors. With over 2,000 employees, that include hardware and software engineers, Evertz is one of the leaders in the broadcast industry. Evertz has a global presence with offices located in: Canada, United States, United Kingdom, Germany, United Arab Emirates, India, Hong Kong, China,, and Australia. Evertz was named one of Canada’s 50 Best Managed Companies, which recognizes excellence in Canadian-owned and Canadian-managed companies. Canada’s 50 Best Managed Companies identifies Canadian corporate success through companies focused on their core vision, creating stakeholder value and excelling in the global economy.