Machine Learning Engineer

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.

Responsibilities

• Design 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 classification
  • Query routing
  • Entity extraction
  • Application detection
  • Evidence retrieval
• Build and maintain train, test, and evaluation pipelines with strong focus on:
  • Accuracy and F1 score
  • Confidence scoring and calibration
  • Latency and throughput
• Optimize models to meet strict constraints:
  • Sub-second inference latency
  • CPU-only execution
  • Compact model size (<500MB)
• 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 alerting
  • Drift detection
  • Retraining pipelines
  • Data feedback loops
• Develop strategies to handle domain evolution, including:
  • New agents / skills
  • New entity types
  • Updates to domain definitions
• Leverage 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 Skills

• Strong expertise in Machine Learning and Applied NLP, especially in:
  • Text classification
  • Intent detection
  • Query routing
  • Entity extraction
  • Semantic similarity and retrieval
• Proven experience with non-LLM approaches, including:
  • Encoder-based models
  • Embedding-based pipelines
  • Classical ML (e.g., XGBoost, Logistic Regression)
  • Lightweight deep learning models
• Experience designing training datasets, labeling frameworks, and structured output schemas for multi-task NLP systems.
• Strong understanding of data preprocessing and quality improvement, including:
  • Normalization
  • Deduplication
  • Class imbalance handling
  • Synthetic data generation
• Experience building robust evaluation frameworks, including:
  • Precision, Recall, F1
  • Confidence scoring
  • Ranking quality
  • Latency measurement
• Hands-on experience with entity extraction for structured enterprise domains, such as:
  • Device identifiers (PID, Serial Number, MAC, Hostname)
  • Smart / Virtual accounts
  • Orders, contracts, subscriptions
  • Product families and licenses
• Experience 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 observability
  • Drift detection
  • Retraining pipelines
  • Model lifecycle management
• Strong 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.