Technergetics
AI/ML Engineer III
About this role
Technergetics seeks an AI/ML Engineer III to design and deploy advanced AI systems including foundation models, agentic workflows, and multimodal ML for government and commercial customers. This full-time role involves building production systems, optimizing inference, and mentoring junior team members in a hybrid or remote capacity (contingent on contract award).
What you'll do
- Design and deploy multi-modal ML architectures and agentic systems on top of LLMs for operational use
- Implement and optimize retrieval-augmented generation pipelines grounded in authoritative data sources
- Define evaluation metrics and monitor model/agent behavior in production environments
- Optimize model inference for production and edge/DDIL deployment scenarios
- Develop data pipelines and preprocessing solutions for ML tasks
- Lead, mentor, and coach junior data scientists and engineers
What they're looking for
- Large language models and foundation models
- Agentic AI systems and agent frameworks
- Retrieval-augmented generation (RAG)
- Multi-modal machine learning
- Model optimization and inference tuning
- Data pipeline development
- Cloud architectures and deployment
- Python and ML libraries (PyTorch, TensorFlow, etc.)
Benefits
- Relocation signing bonus available for local candidates
- Hybrid work with 20% onsite for Utica/Rome-area employees
- Access to company and AFRL facilities
- Work with government and commercial customers on mission-critical projects
- Opportunity to mentor and lead junior team members
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Technergetics
Technergetics advances quantum computing and quantum networking technologies through research partnerships with government agencies like AFRL and DARPA. The company is hiring systems engineers and photonics engineers to evaluate fault-tolerant quantum systems, design integrated photonic devices, and drive the practical application of quantum technologies.
View all jobs at TechnergeticsLikely interview questions
- Describe your experience building or deploying agentic systems with large language models—what frameworks did you use and what challenges did you encounter?
- How have you designed and evaluated retrieval-augmented generation pipelines, and how did you measure their effectiveness against baseline approaches?