MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Adversarial ML Engineer red-teaming foundation models and AI systems for client security teams. Investigating vulnerabilities, designing attacks and defenses, and validating remediation across model and agentic layers.
Responsibilities
- Conduct hands-on adversarial testing across models, applications, agentic layers, and data pipelines, including multi-turn jailbreaks, guardrail bypasses, prompt injection, agent and tool-chain misuse, dangerous-capability evaluation, API abuse, data poisoning, model inversion, and membership inference.
- Investigate edge-case findings from AI red-team campaigns and turn flagged anomalies into understood, reproducible vulnerabilities.
- Produce severity-ranked findings mapped to the OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework and Generative AI Profile, MITRE ATLAS, and EU AI Act Article 55 expectations.
- Provide evidence and clear reproduction steps for identified vulnerabilities.
- Deliver practical remediation guidance and conduct retesting to verify fixes.
- Work closely with the client’s Guardrails and AI red-teaming team.
- Translate technical findings into clear language for both engineers and non-technical stakeholders.
- Remain engaged through remediation and final retesting.
Requirements
- Expert-level Python programming with deep proficiency in ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers
- Hands-on experience fine-tuning ML models and Small Language Models (SLMs), including LoRA/QLoRA, PEFT, instruction tuning, and domain adaptation, for performance and robustness objectives
- Strong foundation in ML mathematics: optimization, linear algebra, probability, and statistics
- Proven ability to design and execute adversarial attacks, including evasion, data poisoning, model extraction, and membership inference
- Experience implementing defenses such as adversarial training, robust fine-tuning, input sanitization, and differential privacy
- Proficiency with adversarial ML toolkits such as Adversarial Robustness Toolbox (ART), CleverHans, and Foolbox
- Experience red-teaming AI/LLM systems, including prompt injection, jailbreak testing, and safety/alignment evaluation
- Ability to evaluate and benchmark model robustness, safety, and security posture before and after fine-tuning
- Familiarity with MLOps practices, including model versioning, experiment tracking, and secure deployment pipelines
- Strong threat-modeling skills and an attacker’s mindset, with the ability to communicate risks clearly to technical and non-technical stakeholders
- Active awareness of the latest adversarial ML and GenAI security research
Benefits
- Fully remote working anywhere in the Canada, built around delivery rather than presence.
- A clear path to grow into staff and principal-level technical influence.
- Full support from C-Serv across the hiring process and beyond, with full-cycle accountability.
- A values-led, woman-owned delivery partner built on empathy, integrity, collaboration, and growth.
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