Responsibilities

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.

Job Type: Full-Time, Permanent