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Pre-Sales GenAI Architect / FDE

Inferdat
Remote

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Job Details

Contract
$50 - $70 an hour

Benefits

Work from home

Qualifications

AI models
Generative models
System design
AI platforms (beyond public GPTs)
Prompt engineering
System design for system development
Machine learning cloud services
AWS
Machine intelligence
Cloud Architecture Design (Architecture design skills)
Generative AI
Client interaction via phone calls

Full Job Description

About Inferdat

Inferdat is an AWS Advanced Tier partner with AI Competency status, focused on getting GenAI systems into production. We work with companies from first scoping call to live deployment using ProdWorks, our 4-stage delivery process that consistently cuts deployment timelines to 6-10 weeks. Our platform, ABI, runs on Amazon Bedrock and AWS Strands Agents SDK and is deployed directly into customer AWS accounts.

This is a startup. Early team members shape how we work and what we build.

The Role

Pre-sales at Inferdat means two things: helping prospective customers understand what GenAI can do for their specific situation, and scoping the work accurately enough that delivery can execute without surprises.

You will run SHOW sessions, our 1-hour live demos where stakeholders see a production-grade GenAI system running their industry's use case. When bandwidth allows, you will also execute PROVE engagements, 1-5 day paid proof-of-value builds on the customer's actual data and environment.

The output of your work feeds directly into Statements of Work. Getting the scope right is part of the job. A bad SOW creates change orders. We avoid those.

What You Will Do

Run discovery and scoping calls across a range of stakeholders: founders, CTOs, VPs at mid-market and enterprise companies
Lead structured data readiness assessments to surface blockers before any build commitment is made
Build and iterate on proof-of-value systems quickly, using the customer's real data
Design and communicate solution architectures at a level of detail that gives both the customer and the delivery team full clarity
Write SOWs that are specific enough to deliver without change orders
Stay current on Amazon Bedrock, AgentCore, Amazon Quick, and related AWS GenAI services

What We Are Looking For

Required:

3+ years working in AI or cloud solution architecture, with real hands-on experience building systems (not just advising on them)
Proven ability to run technical discovery with non-technical buyers and translate findings into scoped work
Hands-on experience with Amazon Bedrock, including model selection, prompt engineering, and RAG pattern implementation
Understanding of agentic AI patterns: tool use, memory, multi-agent coordination
Strong prompt engineering skills and familiarity with running evals against customer data
Ability to build a working POC fast, then explain what you built and why to a mixed technical and business audience
US-based with availability for customer-facing calls during standard business hours

Preferred:

AWS Certified Machine Learning Specialty or AWS Generative AI Professional certification
Familiarity with Amazon Bedrock AgentCore, Amazon Quick, and AWS Kiro (awareness of these is enough; deep experience is a plus)
Experience at an AWS consulting partner or SI
Background working across deal sizes from startup founders to enterprise technical leadership

Compensation and Path

This role starts as an hourly contract at $50-$70/hr depending on experience. The intent is full-time employment. Timeline for that conversion depends on business growth and fit, both of which we will be direct about from the start.

There is no bureaucratic pre-sales process here. You will work closely with sales leadership and have direct input on how the pre-sales motion evolves. As the team grows, this role is positioned to lead pre-sales technically.

Pay: $50.00 - $70.00 per hour

Benefits:

Work from home

Application Question(s):

(AI generated responses are detected and will immediately disqualify the candidate)

Describe a GenAI system you built using Amazon Bedrock. What model did you use, what was the architecture, and what was the hardest technical problem you ran into?

(AI generated responses are detected and will immediately disqualify the candidate)

Have you ever written a scope of work or statement of work for a technical engagement? Walk us through how you approach it and what you do to avoid scope creep after signing.

(AI generated responses are detected and will immediately disqualify the candidate)

Tell us about a time you ran a scoping or discovery call where the customer's stated problem turned out to be different from the actual problem. What did you uncover and how did you handle it?

Work Location: Remote

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