- Company Name
- Jaxon.AI
- Job Title
- Machine Learning Engineer - Model Verification & Testing
- Job Description
-
Job Title: Machine Learning Engineer – Model Verification & Testing
Role Summary:
Design, implement, and maintain rigorous verification, validation, and testing pipelines for NLP and LLM systems in high‑assurance defense contexts. Lead automated evaluation suites, adversarial and regression testing, and acceptance gate management to certify models for secure deployment.
Expectations:
- Deliver repeatable, reproducible ML pipelines with full traceability.
- Collaborate with cross‑functional, distributed teams to integrate models into existing workflows.
- Apply strong governance, guardrails, and auditability to all testing artifacts.
- Maintain high coding standards, documentation, and continuous integration practices.
Key Responsibilities:
- Develop unit, integration, and system tests for data pipelines and ML models.
- Build automated evaluation frameworks, including A/B, canary, fuzzing, and adversarial suites.
- Define and enforce acceptance criteria and metrics for model deployment.
- Optimize data preprocessing, feature engineering, and model parameters for performance and reliability.
- Package and deploy models using containerization, Kubernetes, and cloud services (AWS, GCP, Azure).
- Track test results with monitoring/logging (Prometheus, ELK) and integrate with CI/CD.
- Collaborate with security and compliance teams to meet defense‑grade standards.
Required Skills:
- Strong proficiency in Python, including enterprise practices, modular design, pytest/unittest, and CI/CD.
- Expertise with NumPy, Pandas, scikit‑learn, PyTorch, TensorFlow, and Hugging Face Transformers.
- Experience with LLM deployment, fine‑tuning, and parameter optimization (Ollama, Hugging Face).
- Familiarity with LangChain, Langraph, LlamaIndex, or related LLM frameworks.
- Knowledge of NLP fundamentals (tokenization, embeddings, sequence modeling) and applied tasks (QA, summarization, RAG).
- Container orchestration skills: Docker, Docker Compose, Kubernetes.
- Cloud deployment and monitoring (AWS/GCP/Azure, Prometheus, ELK stack).
- MLOps expertise: reproducible pipelines, model versioning (MLflow, DVC), packaging, and CI/CD integration.
- Proven ability to build and run V&V tooling for ML (test harnesses, metric gates, adversarial testing).
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, or related field.
- Certifications in cloud platforms (AWS, GCP, Azure) or MLOps are a plus.