Senior ML Engineer

Basically we need an senior Architect/Lead-level AI/ML resource with strong expertise in Applied NLP, Machine Learning, and Data Science to design and build scalable enterprise-grade query understanding and intelligent routing solutions. The ideal candidate should have hands-on experience in recommendation systems, forecasting, entity extraction, semantic retrieval, and low-latency ML systems, along with exposure to LLM fine-tuning, RAG, and modern AI architectures. This role requires both deep technical capability and architectural leadership to drive current implementation needs as well as future AI/LLM initiatives.

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
Design and implement advanced ML/Data Science solutions for enterprise use cases, including:
Recommendation systems
Forecasting and predictive analytics
Behavioral and usage pattern analysis
Lead experimentation and implementation of LLM-based solutions, including:
Fine-tuning and optimization of foundation models
Prompt engineering and retrieval augmentation strategies
Evaluation and benchmarking of LLM performance for enterprise workflows
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.
Provide technical leadership and architectural guidance across ML, NLP, and AI initiatives, mentoring engineers and driving scalable enterprise-grade AI solution design.
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
Strong Data Science background with hands-on experience in:
Recommendation systems
Forecasting and predictive modeling
Statistical analysis and feature engineering
Time-series analysis and predictive analytics
Proven experience with non-LLM approaches, including:
Encoder-based models
Embedding-based pipelines
Classical ML (e.g., XGBoost, Logistic Regression)
Lightweight deep learning models
Hands-on experience with LLM technologies, including:
Fine-tuning open-source or enterprise LLMs
Retrieval-Augmented Generation (RAG)
Prompt engineering
Model evaluation and optimization
Understanding of transformer architectures and embedding 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.
 Experience working in architect-level or technical lead roles, driving end-to-end AI/ML solution design, scalability, and technical direction across cross-functional teams.
Prior experience in enterprise support systems, operational routing, licensing platforms, or device/account management domains is highly preferred.