Senior AI Engineer
First seen by Alion on Oct 7, 2026.
Evertz builds the hardware and software behind live television, film production, and streaming worldwide. Our AI team applies generative AI and machine learning across Evertz, from hardware design validation, manufacturing test and quality-control automation, and audio and video processing to AI agents and knowledge retrieval that teams across the company use every day.
We are hiring a Senior AI Engineer to build and run production AI systems across this portfolio and help set the team's technical direction. This is a hands-on role: you own solutions end-to-end, from problem framing with domain experts through deployment and support.
What you will do:
Own AI applications and reusable services end-to-end: 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.
Turn messy inputs (datasheets, schematics, test logs, media, business data) into grounded AI capabilities, including the ingestion and parsing pipelines behind them.
Integrate AI with engineering tools, manufacturing and test systems, and business applications via APIs and MCP servers, with proper validation, security, and human oversight.
Apply classical ML where it fits (classification, anomaly detection, computer vision, audio processing), often on noisy or limited datasets, and deploy and monitor those models alongside LLM systems.
Build repeatable evaluations and use them 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 with the engineering manager: propose architectures, set reusable patterns, and evaluate new models and tooling.
Collaborate with stakeholders to clarify requirements and priorities; mentor engineers through code and design reviews.
What you will bring:
5+ years of software engineering experience, including delivering and supporting production LLM applications beyond proof-of-concept.
Strong Python and backend skills: API design, testing, debugging, and maintainable software.
Hands-on experience with LLM APIs, prompt design, and tool-enabled agents (LangChain, LangGraph, or equivalents).
Practical RAG experience: embeddings, vector and hybrid search, reranking, and measuring retrieval and answer quality.
Solid ML fundamentals: data processing, feature engineering, model training and evaluation, and shipping non-LLM models to production using PyTorch, scikit-learn, NumPy, and pandas.
Experience deploying cloud applications, preferably on Azure, with containers, CI/CD, infrastructure as code, and monitoring.
Strong SQL skills, preferably PostgreSQL, and a working understanding of authentication, access control, and secure data handling.
Clear communication of technical trade-offs to technical and non-technical audiences; able to contribute independently and mentor others.
If you meet most of these, we encourage you to apply.
Nice to have:
AI or ML applied to manufacturing, hardware design, automated inspection or test, or audio/video processing.
Multimodal models, computer vision, anomaly detection, or audio and signal processing.
MCP server development, LLM observability tools (LangSmith, Langfuse), or model serving (vLLM, Ollama).
Fine-tuning open-weight models; helping non-technical teams adopt AI tools.
Bachelor's or Master's in Computer Science, Engineering, or a related field, or equivalent experience.
Recruitment process:
Screening with a recruiter (60-90 min)
Interview with Hiring Manager (60 min)
Technical interview with 2 Senior Engineers (60 min)
Tech stack
- AI applications
- Python
- LLM Workflows
- ML fundamentals
- SQL
- Cloud Applications
- LLM APIs