Machine Learning
Model Engineering
Machine Learning Model Engineering We design, train, fine‑tune, evaluate, and deploy models that power real products. From classic ML (XGBoost, LGBM) to LLM fine‑tuning and distillation, we build for quality, latency, and cost.
- / Trust By
Model‑agnostic (gpt5, Grok) with proven ML stack (PyTorch, scikit‑learn, XGBoost, LightGBM).
We engineer models with evals, observability, and MLOps baked in.
KPI-driven
Reliable
Deployable
- / Techstack
Our toolkit
What we ship
End‑to‑end: data → model → evals → serving → monitoring. Production‑first from day one.
Data & Feature Engineering
ETL/ELT, quality checks, feature stores, and labeling workflows.
Supervised & Unsupervised Models
Forecasting, ranking, classification, clustering, anomaly detection.
LLM Fine‑tuning & Distillation
LoRA/QLoRA, supervised finetune, DPO/RLHF, safety alignment.
Evaluation & Benchmarking
Pass@k, ROC‑AUC, F1, BLEU/ROUGE, cost/latency SLOs, ablations.
Serving & Deployment
Batch, realtime, streaming; FastAPI, Triton/vLLM, ONNX/TensorRT.
Monitoring & Drift
Data/label drift, bias/fairness, canary, regression suites, alerting.
Outcomes you can expect
Quality
Evaluated against task‑specific metrics (AUC/F1/Recall, pass@k)
Latency & Cost
Throughput/SLA tuning, quantization, caching, and batching
Reliability
Canaries, regression tests, rollbacks, and observability
Adoption
Clear APIs, docs, and dashboards for business & engineering
Our delivery process
- / Process
1/6
Business goals, constraints, data mapping, and success metrics.
Discover
2/6
Model choices, features, eval plan, serving strategy, and SLAs.
Design
3/6
Training pipelines, finetuning, ablations, and automated tests.
Develop
4/6
Profiling, optimization, safety, canary, and monitoring hooks.
Harden
5/6
Batch/online serving with CI/CD and infra as code.
Deploy
6/6
Feedback loops, drift handling, and roadmap of next wins.
Improve
Who we build for
From visual search in ecommerce to defect detection in factories — tailored to your domain.
Ecommerce
Fintech
Operations
Customer Support
Marketing
HR & IT
Logistics
Healthcare
- / FAQs
Frequently Asked Questions
- Do you need our production data?
We start with a sanitized dataset or synthetic sample. PII is masked and policies are enforced.
- Cloud vs on‑prem?
We deploy to your cloud (AWS/GCP/Azure) or VPC. Artifact registries and secrets are managed via least‑privilege.
- How do you ensure model fairness?
Bias checks, subgroup metrics, human review, and guardrails in the evaluation plan.
- How quickly can we see results?
Initial baselines within 2–4 weeks; optimized models and serving plan in 4–8 weeks depending on scope.
Ready to ship better models?
We’ll align on KPIs, build robust pipelines, and deploy models that your team can trust and extend.