Pre-vetted AI/ML and data professionals placed on your timeline. Every consultant we put forward has genuine hands-on depth — not keyword matches.
From LLM and agentic AI engineers to ML platform and data governance practitioners — we cover the roles that are hardest to find.
| Role Type | Specializations |
|---|---|
| ML / AI Engineers | Model training, fine-tuning (full, LoRA, QLoRA, PEFT), RLHF, inference optimization, model evaluation |
| LLM Engineers | Prompt engineering, RAG architecture, LLM evaluation, agentic systems, tool-use pipelines |
| Data Scientists | Predictive modeling, experimentation & A/B testing, causal inference, applied statistics |
| Data Engineers | Pipeline architecture, Spark / Databricks / dbt, real-time streaming (Kafka, Flink), lakehouse design |
| Analytics Engineers | Semantic layer design, dbt, Looker / Tableau / Power BI, data modeling |
| ML Platform Engineers | MLflow, Kubeflow, SageMaker, feature stores, model registries, CI/CD for ML |
| Data Governance | MDM, data quality, lineage tracking, CCPA / HIPAA compliance, data cataloging |
We source AI/ML talent nationally — engineers who've shipped production LLMs at major tech firms, built ML infrastructure at scale, and contributed to open-source AI tooling. Not candidates with "AI" on a resume.
We don't send a stack of resumes. We send the right candidates — after doing the technical vetting ourselves.
We start with a short call to understand your stack, the problem you're solving, team structure, and what "good" looks like technically. No generic job description intake.
We identify and technically screen candidates from our nationwide AI/ML network. You receive a short, curated shortlist — typically 2–3 candidates — within days, not weeks.
Once you select, we handle contracting logistics and onboarding coordination. The consultant integrates with your team on your terms — short sprint, long project, or ongoing.
A single specialist placed into your existing team. Ideal for backfills, skill gaps, or focused project work on a defined timeline.
Multiple consultants embedded into your team, working alongside full-time staff. Scales your AI capability without scaling your headcount permanently.
A full squad of consultants mobilized for a defined initiative — data engineer, ML engineer, and analytics engineer working together on your roadmap.
Whether you're running a fine-tuning project, building a RAG pipeline, or standing up an ML platform — we place practitioners who've done exactly that work in production.
| Project Type | What our consultants bring |
|---|---|
| LLM Fine-Tuning | Domain-specific model adaptation using LoRA, QLoRA, PEFT, and full fine-tuning. Includes dataset preparation, training runs, evaluation benchmarking, and deployment. |
| RAG Systems | Retrieval-augmented generation pipelines — chunking strategy, embedding selection, vector store setup, re-ranking, evaluation frameworks, and production guardrails. |
| Agentic AI Systems | Multi-agent orchestration, tool-use pipelines, planning and reasoning systems, and memory management for production-grade agentic applications. |
| AI Product Engineering | Model serving infrastructure, latency optimization, A/B testing frameworks, shadow deployment, and monitoring pipelines for production AI systems. |
| ML Platform Builds | Feature stores, training pipeline orchestration, experiment tracking, model registries, and CI/CD for ML — the infrastructure that makes models reproducible and deployable. |
| Data Platform Engineering | Lakehouse architecture, streaming ingestion pipelines, semantic layers, and data quality frameworks — the data foundation AI systems actually need. |
Tell us the role, the timeline, and the tech stack. We'll come back with a curated shortlist — not a resume dump.