We are seeking a skilled ML Engineer to design, build, and deploy advanced ML solutions that drive analytics, anomaly detection, and data classification across enterprise-scale environments.
This role will focus on developing and optimizing models, including LLMs and prompt engineering pipelines, and ensuring seamless integration into production workflows spanning cloud-native, on-premises, and Databricks ecosystems.
Key Responsibilities :
Design, build, and deploy ML models for user behavior analytics, anomaly detection, and data classification across enterprise environments.
Collaborate with data scientists, software and data engineers to integrate ML models into production pipelines, cloud-native environments, on-premises, and Databricks workflows.
Develop, fine-tune, and evaluate LLMs and prompt engineering solutions for data classification, labeling, and threat analysis features.
Optimize models using techniques like distillation, quantization, and efficient data structures to boost performance and lower resource cost.
Build high-performance data inputs using embeddings, vector databases, and distributed training frameworks.
Manage model lifecycle and performance via MLOps best practices: monitoring, retraining, and deploying updates.
Partner with data scientists, cybersecurity researchers and product teams to evaluate and refine ML-driven capabilities.
Conduct experiments and benchmark results in fast-paced, data-intensive environments.
Requirements: Bachelors degree in computer science, data science, or related field.
3+ years of experience in Machine Learning engineering or ML-adjacent roles (data science, MLOps, AI).
Strong programming proficiency in Python, familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Hands-on experience with LLMs, prompt engineering, vector embedding techniques, or related technologies.
Proficiency with big data platforms like Databricks, PySpark, and cloud services (Azure, AWS)
Experience with MLOps tools and deployment (CI/CD, containerization, Kubernetes, etc.).
Experience with vector DBs, retrieval-augmented generation (RAG) frameworks like Langchain
Solid analytical and debugging skills with ability to translate research insights into production code.
Nice-to-have:
Prior experience working on cybersecurity or data protection products
Familiarity with user behavior-based threat detection, anomaly detection, or metadata analytics
Statistical modeling and prompt evaluation ability (e.g., response coherency, relevance).
This position is open to all candidates.