ML Engineer

We are looking for an ML Engineer - ML-2 with around 3 years of experience to build and scale production-grade machine learning and AI systems at Matters. You will work on challenging problems across document intelligence, data classification, sensitive information detection, behavioral analytics, anomaly detection, and agentic AI. Our systems operate on large enterprise datasets, requiring a strong focus on scalability, performance, reliability, distributed systems, and efficient AI architectures. You will work closely with senior AI engineers, security engineers, and product teams to take ML solutions from experimentation to production.

Responsibilities:

• Design, develop, and deploy scalable ML solutions for real-world data security use cases.
• Build hierarchical document classification systems to identify document types and sub-types from large, unstructured datasets.
• Develop ML pipelines to identify PII, sensitive information, credentials, and secrets across enterprise data stores.
• Build user and entity behavioral profiling systems, including behavioral baselines, anomaly detection, and identification of abnormal activities.
• Develop LLM-powered RAG and multi-agent AI systems that can reason over enterprise data and provide actionable insights.
• Work with large-scale datasets and optimize data processing, training, and inference pipelines.
• Experiment with different ML and statistical approaches, evaluate model performance, and continuously improve accuracy and reliability.
• Design production-ready ML pipelines with appropriate testing, monitoring, and observability.
• Optimize ML systems for scalability, latency, memory, throughput, and cost.
• Collaborate with AI, engineering, security, and product teams to translate complex requirements into reliable ML solutions.
• Stay current with advancements in machine learning, generative AI, LLMs, and agentic AI, and evaluate their applicability to real-world problems.

Requirements:

• 3+ years of hands-on experience in machine learning / AI engineering.
• Bachelor's or master's degree in computer science, artificial intelligence, data science, or a related field.
• Strong proficiency in Python and experience building production ML systems.
• Strong understanding of ML fundamentals, including classification, clustering, anomaly detection, feature engineering, and model evaluation.
• Hands-on experience with NLP, document classification, information extraction, or related ML problems.
• Experience with ML model development, experimentation, evaluation, and deployment.
• Experience working with large-scale data and building efficient data processing pipelines.
• Strong problem-solving and analytical skills.
• Good understanding of software engineering practices, Git, testing, and code quality.

Preferred Qualifications / Bonus Skills:

• Experience in anomaly detection, behavioral analytics, cybersecurity, or data security.
• Experience with LLMs, RAG, embeddings, vector databases, and agentic AI.
• Experience with LangChain, LangGraph, Hugging Face, or similar frameworks.
• Experience with PII, sensitive data, or secret detection.
• Knowledge of MLOps tools such as MLflow, Kubeflow, or SageMaker.
• Experience with distributed systems and scalable AI architectures.
• Experience optimizing ML systems for latency, throughput, memory, and cost.
• Experience with AWS/GCP, Docker, Kubernetes, and CI/CD.
• Strong understanding of software engineering and production system design.

Technical Skills:

• Programming: Python, Rust, TypeScript, SQL, Git.
• Machine Learning: Scikit-learn, PyTorch, XGBoost, NLP, Classification, Clustering, Anomaly Detection.
• Generative AI: LLMs, Prompt Engineering, RAG, Embeddings, Vector Databases, AI Agents.
• Databases: PostgreSQL, MongoDB, and Vector Databases (Pinecone, Weaviate, and Chroma).
• MLOps and Infrastructure: MLflow, Kubeflow, SageMaker, AWS/GCP, Docker, Kubernetes, CI/CD.
• System Design: Distributed Systems, Scalable AI Architectures, Model Serving, Monitoring, and Observability.