Description
Are you excited about helping the world’s largest Private Equity firms harness Generative AI to transform how their portfolio companies build, operate, and scale? We’re looking for a GenAI Solutions Architect to join the AWS Private Equity team, a small, high-impact group that works directly with PE firms and their portfolios to accelerate AI adoption and drive measurable business outcomes.
You’ll sit at the intersection of business strategy and AI technology. Your customers are PE operating partners, CTOs, and C‑suite executives who are making multi‑million dollar bets on AI, and they need a trusted technical advisor who can translate GenAI capabilities into real value creation across dozens of portfolio companies.
This isn’t a typical SA role. You’ll engage across the full PE lifecycle, from evaluating AI readiness during technical due diligence to designing scalable GenAI strategies that PE firms can replicate across their entire portfolio. You’ll lead hackathons, architect production AI systems, build compelling demos, and help PE firms establish AI as a core value creation lever. Your work will span use cases from AI‑powered software development and code generation to document intelligence, agentic workflows, and industry‑specific AI applications.
You’ll work with AWS GenAI technologies including Amazon Kiro, Amazon Bedrock, and Amazon SageMaker, along with the full breadth of AWS AI services. What matters most is your ability to connect technology to business outcomes that PE firms care about: faster time to value, operational efficiency, and competitive differentiation across their portfolios.
Key job responsibilities
Own the technical AI relationship with PE firms and portfolio company leadership, operating as their trusted GenAI advisor
Design and architect production GenAI solutions using Amazon Bedrock, SageMaker, Kiro, and other AWS AI services, with a focus on measurable business outcomes
Lead scalable, repeatable GenAI engagements across PE portfolios including hackathons, use case identification workshops, AI maturity assessments, and business case development sessions
Support PE firms in technical due diligence by evaluating target companies’ AI readiness and identifying GenAI value creation opportunities pre‑ and post‑acquisition
Architect systems leveraging LLMs, RAG, vector databases, agentic workflows, and fine‑tuning techniques for production workloads
Build compelling demos and proof‑of‑concepts that demonstrate ROI for GenAI use cases including software development productivity, document intelligence, conversational AI, and agent‑based systems
Develop scalable GenAI strategies that PE firms can deploy across their portfolio companies (think frameworks, not one‑offs)
Participate in PE firm business reviews, portfolio reviews, and annual planning to align GenAI strategies with investment objectives
Evangelize AWS GenAI technologies through conferences, workshops, white papers, blog posts, and thought leadership
Create reusable assets (reference architectures, code samples, best practices) that scale impact beyond individual engagements
Coordinate with SI partners, ISV partners, and internal AWS teams to deliver customer outcomes
Contribute to team growth through hiring, coaching, and mentoring
Basic Qualifications
Experience developing, deploying and managing AI products at scale
Experience building complex software systems that have been successfully delivered to customers, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
Experience in a customer‑facing role, engaging with customer executives, technologists or partners to solve business problems with advanced technologies
Master’s degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and PyTorch
Excellent communication skills with the ability to translate complex AI concepts for both technical and business audiences
Preferred Qualifications
Experience leading the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations, or experience programming with at least one software programming language
Knowledge of compliance and security standards across the enterprise IT landscape
Experience working with or within Private Equity firms, Venture Capital, or Investment Banking, particularly supporting due diligence, post‑acquisition value creation, or technology strategy
Experience with AWS AI services (Amazon Bedrock, SageMaker, Kiro) or equivalent cloud AI platforms
Experience with MLOps/AIDLC workflows for production AI systems including model deployment, monitoring, and governance
Track record leading large‑scale technical events (hackathons, workshops) with 20‑100+ participants
Understanding of PE workflows including deal sourcing, due diligence, portfolio management, and value creation planning
AWS certifications (AI Practitioner, ML Specialty, Solutions Architect Professional)
Thought leadership through public speaking, technical writing, or open‑source contributions
Willingness to travel to customer locations as needed
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