From Experimentto Production AI
Enterprise MLOps pipelines, production model deployment, LLM application engineering, and AI infrastructure at scale. We transform ML experiments into reliable, cost-efficient, and fully-monitored AI systems.
AI Engineering Services
Every layer of the ML stack — from raw data to production inference, with monitoring throughout.
MLOps Pipelines
CI/CD for ML with automated training workflows, validation gates, production deployment, and rollback safety. Git-style version control for datasets, model artifacts, and hyperparameters with full reproducibility.
LLM Integration & Agents
RAG pipelines with semantic search, multi-model orchestration, prompt optimization, fine-tuning at scale, and autonomous agent frameworks with tool use, memory management, and guardrails.
Model Serving & Inference
Sub-50ms latency inference with GPU/TPU optimization, progressive canary deployments, A/B testing harnesses, shadow mode validation, and dynamic batching for throughput at scale.
Data Engineering & Feature Stores
Enterprise feature stores (Tecton, Feast), vector database integrations (Pinecone, Weaviate, Qdrant), streaming pipelines (Kafka, Kinesis), data lakehouse architecture, and PII anonymization.
Model Monitoring & Observability
Data drift, model drift, prediction drift detection with automated alerts. Performance tracking with SHAP explanations, feature importance shifts, and production anomaly detection triggering retraining.
AI Application Engineering
FastAPI/GraphQL backends with authentication, caching, rate limiting. Frontend integration with real-time streaming, fallback strategies, cost budgeting, and business metrics tracking end-to-end.
Our ML Engineering Process
From raw data to monitored production — a repeatable, rigorous process refined across 150+ deployments.
Discovery & Data Audit
Assess data quality, availability, labeling needs, and regulatory constraints. Define ML problem formulation, success metrics, and baseline benchmarks.
Experiment & Prototype
Rapid experimentation with MLflow tracking. Baseline models, feature engineering, hyperparameter search. Establish validation methodology and reproducibility standards.
Pipeline Architecture
Design production-grade training pipelines, feature stores, and serving infrastructure. Choose orchestration (Kubeflow, Airflow) and serving stack (Triton, Ray, Torchserve).
Training & Optimization
Distributed training, mixed-precision, gradient checkpointing. Model compression: quantization, pruning, distillation. ONNX export and hardware-specific optimization.
Deployment & Canary
Shadow mode validation, canary deployments with traffic splitting, A/B testing harnesses, and automated rollback triggers based on latency and accuracy thresholds.
Monitor & Iterate
Continuous drift detection, automated retraining triggers, cost monitoring, model governance approvals, and ongoing optimization for throughput and latency.
MLOps Pipeline Architecture
End-to-end pipeline from raw data ingestion through automated retraining — every stage observable and reproducible.
ML Tech Stack
Featured AI Project
Real-Time Document Intelligence Platform
Fortune 500 Insurance Company
Challenge
Claims processing took 3–5 days per claim. 200+ analysts manually reading unstructured policy documents. No way to extract structured data at scale. Inconsistent decisions costing $12M/year in manual rework.
Technical Approach
Built a RAG pipeline over 2M policy documents using BGE embeddings + Pinecone. Fine-tuned LLaMA 3 8B with LoRA on 50K annotated claim examples. Served via TGI on EKS with 200ms p99 latency. MLflow for experiment tracking, WhyLabs for drift monitoring.
Tech Stack Used
Outcomes
Certifications & Standards
Meet the AI Engineering Team
Principal ML Engineer
LLM Specialist
Data Engineer (ML)
ML Inference Engineer
Engagement Models
ML Assessment
Production ML Project
ML Engineering Retainer
Technical Questions
Build Production AI Systems
From experiment to production — reliable, scalable, and cost-efficient AI infrastructure. Senior ML engineers, no juniors.