Machine Learning Engineer

We are seeking a highly skilled Machine Learning Engineer to design and build a low-latency query understanding and intelligent routing system that operates without reliance on large language models. The role focuses on extracting intent, entities, application context, routing decisions, and supporting evidence from user queries in real time.This is a full lifecycle role spanning data modeling, ML development, optimization, local deployment, and MLOps. The ideal candidate will have strong experience in applied NLP, lightweight model architectures, and production-grade ML systems , with a focus on sub-second inference, CPU-based execution, and scalable domain evolution .ResponsibilitiesDesign and implement a query understanding pipeline to extract intent, routing decisions, entities, application mapping, and historical evidence from user queries and conversations.Define and build the training data model and annotation schema for structured outputs (intent, routing, entities, applications, evidence).Lead data collection, synthesis, analysis, and cleaning to develop high-quality datasets for model training and evaluation.Develop and evaluate baseline and advanced non-LLM models for:Intent classificationQuery routingEntity extractionApplication detectionEvidence retrievalBuild and maintain train, test, and evaluation pipelines with strong focus on:Accuracy and F1 scoreConfidence scoring and calibrationLatency and throughputOptimize models to meet strict constraints:Sub-second inference latencyCPU-only executionCompact model size (Deploy models locally within the application codebase , ensuring seamless integration without reliance on hosted AI services.Design and implement a Level 4 MLOps framework , including:Monitoring and alertingDrift detectionRetraining pipelinesData feedback loopsDevelop strategies to handle domain evolution , including:New agents / skillsNew entity typesUpdates to domain definitionsLeverage historical queries and routing decisions to improve prediction accuracy and evidence generation.Collaborate with product, engineering, and domain teams to translate business workflows into scalable ML solutions.Deliver a working demo / prototype baseline , and iteratively mature it into a production-ready system.Required SkillsStrong expertise in Machine Learning and Applied NLP , especially in:Text classificationIntent detectionQuery routingEntity extractionSemantic similarity and retrievalProven experience with non-LLM approaches , including:Encoder-based modelsEmbedding-based pipelinesClassical ML (e.g., XGBoost, Logistic Regression)Lightweight deep learning modelsExperience designing training datasets, labeling frameworks, and structured output schemas for multi-task NLP systems.Strong understanding of data preprocessing and quality improvement , including:NormalizationDeduplicationClass imbalance handlingSynthetic data generationExperience building robust evaluation frameworks , including:Precision, Recall, F1Confidence scoringRanking qualityLatency measurementHands-on experience with entity extraction for structured enterprise domains , such as:Device identifiers (PID, Serial Number, MAC, Hostname)Smart / Virtual accountsOrders, contracts, subscriptionsProduct families and licensesExperience handling multi-label and hierarchical classification problems .Strong ability to build low-latency, CPU-optimized inference systems with strict memory and performance constraints.Experience deploying ML models locally or on-prem within application codebases (not limited to cloud-hosted inference).Solid understanding of MLOps practices , including:Monitoring and observabilityDrift detectionRetraining pipelinesModel lifecycle managementStrong programming skills in Python , with hands-on experience in ML/NLP frameworks and pipeline orchestration.Ability to adapt systems to continuous domain changes , including new skills, applications, and entities.Prior experience in enterprise support systems, operational routing, licensing platforms, or device/account management domains is highly preferred.