Lead client conversations, discovery workshops, and solution presentations; act as a trusted technical advisor and the bridge between engineering and the business.
Architect AI-centric solutions — reason about model selection, inference architecture, tuning/optimization, and the systems that serve models in production.
Coach and uplift teams — mentor engineers and associate architects, run design reviews, and raise the technical bar through teaching, not gatekeeping.
Explore and prototype emerging technologies, bringing a spirit of creative, spontaneous problem-solving to engagements where the right answer isn't yet obvious.
Core Technical Competencies
You should bring a strong working foundation across the following — breadth as a generalist, with the ability to go deep wherever a problem demands it:
System design fundamentals — scalability, reliability, data modelling, consistency, latency, and the trade-offs between them.
Cloud architecture and services — ideally AWS — including compute, storage, networking, serverless, and well-architected design principles.
Full-stack development — backend and frontend, APIs, and the ability to read, write, and review production code with credibility.
Software engineering best practices — version control, testing, design patterns, and maintainable, observable systems.
DevOps and platform engineering — CI/CD, IaC, containerization, orchestration, and operational concerns across the delivery lifecycle.
AI & Inference Systems
A solid, current understanding of how modern AI systems are built and operated — not just how to call an API:
Underlying architecture of AI systems and inference pipelines — how models are served, scaled, and integrated into broader applications.
Model lifecycle fundamentals — how model development, fine-tuning, and optimization work, and when each is appropriate.
Practical grasp of inference economics and performance — latency, throughput, cost, quantization, and the levers that move them.
Familiarity with GenAI patterns — retrieval-augmented generation, agentic workflows, evaluation, and grounding/guardrails.
Who You Are
Beyond the skills, we're looking for a particular mindset
A first-principles thinker who questions assumptions and reasons up from fundamentals rather than relying on received wisdom.
A curious generalist and technology enthusiast with a genuine drive to understand how things work under the hood.
A creative, spontaneous problem-solver — comfortable improvising, prototyping, and finding non-obvious paths when the textbook answer falls short.
A clear communicator who is well-spoken and equally effective with deeply technical engineers and non-technical executives.
A natural teacher — someone who enjoys explaining, simplifying, and helping others level up.
Strong attention to detail paired with the judgment to know when to zoom out to the bigger picture.
Experience & Qualifications
6–8 years of professional experience, with a strong foundation as a software developer/engineer.
Demonstrated progression into solution architecture — associate, and ideally senior-associate level, exposure.
A track record of delivering technical solutions in client-facing or product environments.
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
Relevant cloud certifications (e.g., AWS Solutions Architect) are a strong plus.
Nice to Have
Prior consulting or professional-services delivery experience.
Hands-on experience with Bedrock, SageMaker, or comparable ML/GenAI platforms.
Experience presenting at workshops, enabling teams, or producing technical thought-leadership content.
