Hire AI Solutions Engineersin 2 weeks
BearPlex AI solutions engineers sit between sales and engineering: building proofs-of-concept, technical demos, integration prototypes, and the customer-specific implementations that close enterprise AI deals. Engineers who can also talk to customers and ship working code under deadline pressure.
What a AI Solutions Engineer actually does at BearPlex
An AI solutions engineer at BearPlex is the technical bridge between your sales team and prospective customers. The role spans: discovery (technical scoping calls with customer engineering teams), proof-of-concept development (build a working integration with the customer's data and stack in 1-3 weeks), technical demos (live coding for executive audiences), RFP responses (the technical sections), customer-specific implementation work (the first 60-90 days post-contract), and the feedback loop back to product on what real customers need. They've shipped production AI for enterprise customers across Fortune 500 financial services, healthcare, and B2B SaaS: meaning they know what enterprise procurement actually evaluates and how to address it. They're equally comfortable in customer architecture review, on a sales call, and in their IDE shipping code. This is a rare skill set; most engineers can't talk to customers comfortably and most sales engineers can't actually ship code.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Closed $4M+ in enterprise AI deals through POC work for a Series C SaaS: technical demo conversion rate 65% (vs prior 22%) on enterprise opportunities.
Built 8 healthcare AI POCs over 12 months: 6 closed to multi-year contracts, including the largest deal in company history at $2.8M ARR.
Led technical responses for 12 enterprise financial services RFPs: won 7, including 2 displacing established competitors at top-20 US banks.
Built integration POCs for 14 enterprise B2B SaaS customers: average POC duration 8 days, conversion to paid 71%.
Skills matrix
The capabilities every BearPlex AI Solutions Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Technical discovery and customer scoping | Expert | Discovery frameworks · Technical architecture review · Use case validation |
| Proof-of-concept development under deadline | Expert | Rapid prototyping · Customer data integration · Demo-quality code |
| Live technical demos for executive audiences | Expert | Live coding · Architecture explanation · Q&A handling |
| RFP technical response writing | Expert | RFP frameworks · Capability mapping · Compliance responses |
| Customer-specific integration work (first 60-90 days) | Expert | Customer stack integration · Data pipelines · Custom prompt engineering |
| Enterprise security and compliance Q&A | Expert | SOC 2 framework · GDPR / HIPAA / financial compliance answers · Security architecture |
| Multi-cloud deployment (AWS, Azure, GCP) | Advanced | Bedrock, Azure OpenAI, Vertex AI · VPC integration patterns |
| Industry-specific AI (healthcare, financial services, SaaS) | Expert | Sector-specific compliance · Domain-specific architecture |
| Pricing and commercial discussion support | Advanced | Cost modeling · TCO analysis · ROI conversation support |
| Post-sale handover to delivery teams | Expert | Documentation · Knowledge transfer · Internal coordination |
| Customer engineering relationship building | Expert | Technical credibility · Long-term advisor relationship |
| Product feedback loops to engineering | Expert | Customer insight synthesis · Feature prioritization input |
How we vet AI solutions engineers
Technical + customer-facing screen
60-minute interview combining technical depth and customer interaction. We probe: can the candidate ship production code AND talk to a customer about it? Past SEs who've never coded under deadline pressure or never been on a sales call won't survive the role.
Live POC exercise
We simulate a customer scoping call (us as the customer; candidate as the SE) followed by a 90-minute POC build window. Candidate must scope, build, and demo a working POC. We're looking for: technical execution under pressure, scoping judgment, customer-facing communication.
Reference deep-dive on closed deals
Three reference checks focused on actual deals the SE contributed to closing. Past SEs who 'supported' deals without contributing meaningfully are a red flag; SEs whose technical work directly drove closed deals are the green flag.
Reference checks + paid trial
Trial engagement on a real client SE need, typically a customer POC where the SE builds and demos. We don't take engineers off trial until both Hamad and the client engineer report 'I want this person on the team next sprint.'
What clients say
“Their solutions engineer literally won us a $1.4M deal. The customer's CTO was skeptical we could integrate with their stack; our SE built a working POC in 6 days that demonstrated it. Deal closed two weeks later.”
“Best technical SE I've worked with. He knew our products as well as our PMs and could explain architecture trade-offs in customer language. Customer engineering teams ended up requesting him by name on follow-on engagements.”
“We needed an SE who could survive enterprise procurement. Their engineer answered SOC 2, HIPAA, and FedRAMP questions in real-time on calls: capabilities our internal team didn't have.”
Hiring AI solutions engineers: questions answered
Yes: most of our SEs have specific experience with enterprise procurement requirements. SOC 2 control answers, GDPR / HIPAA / FedRAMP responses, security architecture conversations. For deeply regulated sectors, we staff SEs with direct experience in that sector.
Typical: 1-3 weeks from scoping to demo-ready POC. For simple integrations: 5-7 days. For complex multi-system POCs: up to 4 weeks. Our SEs work fast because they've seen many customer integrations and have pattern libraries to draw from.
Embedded with your sales team for the duration of the engagement: they attend your sales calls, work in your CRM, and align with your sales process. We're not a separate contractor that hands deliverables over a wall; we work as your SE function.
Yes: RFP technical sections are a common SE deliverable. We've responded to enterprise RFPs in financial services, healthcare, government, and B2B SaaS. Standard scope: technical capability mapping, security and compliance responses, architecture diagrams, customer reference selection support.
Embedded SE engagement: $30K-$50K monthly retainer (typically 6-18 months). Per-deal POC engagement: $25K-$80K depending on POC complexity (1-4 weeks). Hourly retainer for spot SE support: $300-$500/hour for senior SEs. Most successful engagements are embedded for the duration of an active sales motion.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. For US customer-facing SE work, we typically staff engineers with PST / EST time zones available at premium pricing. For customer-facing roles, time zone overlap with the customer matters more than for backend engineering.
Yes: common pattern is the same SE who built the POC leads the first 60-90 days of implementation post-contract. This continuity is valuable for customer experience and accelerates time-to-production. After the initial implementation phase, we typically transition to dedicated engineering teams.
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