AI creates value when models move from experiment to monitored production — with versioned data, tracked runs, and clear promotion gates. We place AI/ML engineers who build that path end to end, not demos that stall in pilot.
Our approach
We match engineers to your training, serving, and MLOps stack. Screening covers production delivery, evaluation discipline, and cost-aware inference design so models are operable after launch. Engagement model and seniority are agreed before you interview.
Skills we staff
Profiles are matched to your training, serving, and MLOps tools.
- PyTorch & training — Training jobs, evaluation, and model export
- DVC & MLOps pipelines — Data and model versioning for repeatable runs
- MLflow & Weights & Biases — Experiment tracking and promotion records
- Triton & FastAPI serving — Inference services with latency targets
- Hugging Face & LLMs — Fine-tuning and evaluation for LLM features
- RAG & retrieval — Retrieval pipelines and answer-quality checks
- Production monitoring — Drift, latency, and inference cost checks
- Feast & feature stores — Feature definitions for train and serve parity
- ONNX & model export — Portable export for your serving runtime
Outcomes teams see
- 5+ years — Average applied ML experience on placements
- 60%+ — Inference cost reduction potential from serving improvements
- 2–4 weeks — Common fine-tune to staging timeline once access is ready
- Tracked experiments — Runs and artifacts comparable and auditable
Next step
Share the use case, stack, and start date. We align engagement terms and send AI/ML engineers ready to ship production models.