{"id":2326115,"url":"https://alion.io/job/matters-ml-engineer-2","title":"ML Engineer","company":{"id":3270,"name":"Matters","domain":"matters.ai","url":"https://alion.io/company/matters","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":51000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Git","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AWS","optional":true},{"name":"Chroma","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"Embeddings","optional":true},{"name":"GCP","optional":true},{"name":"Hugging Face","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"PyTorch","optional":true},{"name":"Rust","optional":true},{"name":"Scikit-learn","optional":true},{"name":"SQL","optional":true},{"name":"TypeScript","optional":true},{"name":"Weaviate","optional":true},{"name":"XGBoost","optional":true}],"status":"live","first_seen_at":"2026-10-12T07:30:01Z","employer_posted_date":null,"last_verified_at":"2026-10-12T07:30:01Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"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.\nResponsibilities:\nDesign, develop, and deploy scalable ML solutions for real-world data security use cases.\nBuild hierarchical document classification systems to identify document types and sub-types from large, unstructured datasets.\nDevelop ML pipelines to identify PII, sensitive information, credentials, and secrets across enterprise data stores.\nBuild user and entity behavioral profiling systems, including behavioral baselines, anomaly detection, and identification of abnormal activities.\nDevelop LLM-powered RAG and multi-agent AI systems that can reason over enterprise data and provide actionable insights.\nWork with large-scale datasets and optimize data processing, training, and inference pipelines.\nExperiment with different ML and statistical approaches, evaluate model performance, and continuously improve accuracy and reliability.\nDesign production-ready ML pipelines with appropriate testing, monitoring, and observability.\nOptimize ML systems for scalability, latency, memory, throughput, and cost.\nCollaborate with AI, engineering, security, and product teams to translate complex requirements into reliable ML solutions.\nStay current with advancements in machine learning, generative AI, LLMs, and agentic AI, and evaluate their applicability to real-world problems.\nRequirements:\n3+ years of hands-on experience in machine learning / AI engineering.\nBachelor's or master's degree in computer science, artificial intelligence, data science, or a related field.\nStrong proficiency in Python and experience building production ML systems.\nStrong understanding of ML fundamentals, including classification, clustering, anomaly detection, feature engineering, and model evaluation.\nHands-on experience with NLP, document classification, information extraction, or related ML problems.\nExperience with ML model development, experimentation, evaluation, and deployment.\nExperience working with large-scale data and building efficient data processing pipelines.\nStrong problem-solving and analytical skills.\nGood understanding of software engineering practices, Git, testing, and code quality.\nPreferred Qualifications / Bonus Skills:\nExperience in anomaly detection, behavioral analytics, cybersecurity, or data security.\nExperience with LLMs, RAG, embeddings, vector databases, and agentic AI.\nExperience with LangChain, LangGraph, Hugging Face, or similar frameworks.\nExperience with PII, sensitive data, or secret detection.\nKnowledge of MLOps tools such as MLflow, Kubeflow, or SageMaker.\nExperience with distributed systems and scalable AI architectures.\nExperience optimizing ML systems for latency, throughput, memory, and cost.\nExperience with AWS/GCP, Docker, Kubernetes, and CI/CD.\nStrong understanding of software engineering and production system design.\nTechnical Skills:\nProgramming: Python, Rust, TypeScript, SQL, Git.\nMachine Learning: Scikit-learn, PyTorch, XGBoost, NLP, Classification, Clustering, Anomaly Detection.\nGenerative AI: LLMs, Prompt Engineering, RAG, Embeddings, Vector Databases, AI Agents.\nDatabases: PostgreSQL, MongoDB, and Vector Databases (Pinecone, Weaviate, and Chroma).\nMLOps and Infrastructure: MLflow, Kubeflow, SageMaker, AWS/GCP, Docker, Kubernetes, CI/CD.\nSystem Design: Distributed Systems, Scalable AI Architectures, Model Serving, Monitoring, and Observability.","description_format":"text","description_chars":3974,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Cloud Security","Information Security","Data Loss 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