Hire Machine Learning Consultants in 2 weeks

Hire an ML engineer when you know what to build and need it built: the model, the pipeline, the serving stack, the evaluation harness. Hire an ML consultant when the decision itself is the problem. Typical triggers for the consultant: a board or investor is asking whether your ML roadmap is credible, you are choosing between building a platform and buying one, an existing ML system is producing results nobody trusts, a regulator or auditor is asking questions your team cannot answer, or you are acquiring a company whose value rests on models you have not inspected. A useful test: if you can write the ticket, you need an engineer. If you cannot write the ticket because the right answer is genuinely unclear, that ambiguity is what a consultant is for. Plenty of engagements start as the second and become the first once the direction is settled.

On BearPlex's published bands: an ML Discovery Sprint of 1 to 2 weeks runs $2,500 to $5,000, and ML architecture reviews and strategy engagements are scoped in pod-time at roughly $9,500 to $28,500 for one to three pod-months depending on scope. A Fractional Head of ML arrangement starts at $3,000 per month depending on time commitment. We quote fixed from a scoping call and bill on outcomes rather than hours, because hourly billing rewards the wrong behaviour in advisory work. Rates across the wider market vary enormously with seniority and region, so when comparing quotes the thing to pin down is not the rate but the deliverable: what decision will you be able to make at the end that you cannot make now.

ML consultants focus on machine learning specifically (classical ML, deep learning, MLOps, model risk management) while AI consultants focus more broadly across LLM systems, AI products, and AI strategy. Significant overlap; for many engagements either fits. We staff per the specific needs of the engagement.

Yes: common engagement type. We've performed ML technical due diligence on 20+ companies for venture capital, private equity, and strategic acquirers. Standard scope: model audit, MLOps assessment, ML team and capability evaluation, IP and data licensing analysis, recommendations on deal terms.

Yes: for select engagements. We have a small bench of senior consultants who do these arrangements (typically 0.4-0.6 FTE) for AI-heavy product teams that need senior ML leadership without full-time hire. Hamad Pervaiz personally takes a small number of these engagements.

Yes: common engagement scope. We've designed and audited MRM frameworks for OCC 2011-12 / SR 11-7 (banking), Federal Reserve supervisory guidance, sector-specific frameworks (FDA SaMD for healthcare, NAIC ORSA for insurance). Our MRM work has passed first-line and second-line review at major US banks.

Hamad Pervaiz (Founder & CEO) is based in Lahore, Pakistan with regular travel to Tokyo and the US. Our broader consultant team is globally distributed. For US-based engagements requiring more synchronous work, we have consultants in PST / EST time zones.

Generalist depth across classical ML, deep learning, MLOps, modern LLM systems. Sector-specific expertise in financial services (banking, asset management), healthcare (clinical ML, FDA SaMD), retail / ecommerce (recommendations, personalization), B2B SaaS (multi-tenant ML). For other sectors we staff with consultants who have direct sector experience.

We're engineers, not deck-makers. Our consultants have shipped production ML themselves and bring those scars to advisory work. We optimize for outcomes that age well, not for engagement length. For ML-heavy strategic decisions, this depth matters more than the brand and breadth that traditional consultancies offer.