Role - Senior Solutions Architect (Human Data Services - Generative AI)
Compenstaion - $160,000 - 190,000 OTE (base + variable)
At iMerit , we help some of the world's leading AI and technology companies build accurate, reliable, and scalable AI solutions. Our expertise spans high-quality AI data, human-in-the-loop workflows, and enterprise AI applications, enabling organizations to accelerate the development of cutting-edge AI systems. If you're passionate about shaping the future of Generative AI and enjoy solving complex technical challenges, we'd love to hear from you.
About the Role
We’re looking for a Senior Solutions Architect – Human Data Services (Generative AI) to design and stand up human-in-the-loop data systems for frontier AI labs and enterprise ML teams .
You’ll work across SFT, RLHF, evaluation, red teaming, multimodal annotation, and agentic product deployments . The work is highly hands-on: defining task schemas, quality frameworks, and data specifications, and ensuring these workflows operate effectively in production.
This role sits at the intersection of pre-sales, solution design, and early delivery . You’ll need to be technically credible with ML researchers, comfortable working hands-on with data and tooling, and able to make thoughtful trade-offs across quality, cost, and speed .
You’ll also apply these capabilities to enterprise AI deployments , including regulated environments, with particular relevance to Banking, Financial Services, and Insurance .
What You’ll Do
Solution Design & Pre-Sales
- Translate ambiguous frontier AI use cases into scoped, buildable data workflows.
- Map enterprise AI use cases to data, evaluation, and human-in-the-loop requirements, including regulated or high-stakes deployments.
- Design end-to-end workflows covering task schemas, guidelines, quality frameworks, sampling strategies, and delivery models.
- Lead technical discovery and identify constraints around data availability, task design, talent requirements, and tooling.
- Produce clear proposals that align scope, pricing, and delivery assumptions.
Data Quality & Evaluation
- Build quality frameworks, including rubrics, acceptance criteria, and audit models.
- Design evaluation approaches for subjective and high-ambiguity tasks, including reasoning, multimodal, and safety/policy use cases.
- Contribute to red teaming and adversarial testing aligned with deployment risks.
Hands-On Execution
- Analyze datasets, validate outputs, and debug workflows.
- Prototype task designs and evaluation pipelines using Python, Ango Hub, or client tooling .
- Partner with Delivery teams to stand up workflows and remain engaged through pilot and early production.
Client & Internal Leadership
- Act as the technical counterpart to ML researchers, product leads, program managers, and enterprise stakeholders.
- Drive alignment across Sales, Solutions, Product, and Delivery .
- Contribute to reusable patterns for workflows, pricing, and quality frameworks.
Experience
- 5–10 years of experience in AI/ML data systems or human-in-the-loop environments, or an advanced degree plus 3–5 years of relevant experience.
- Experience across at least two of the following:
- Annotation
- Data generation/collection
- Evaluation
- RLHF
- Red teaming
- Client-facing experience in a technical role.
Technical Skills
- Strong Python skills with the ability to analyze datasets, prototype workflows, and debug issues.
- Comfortable working with unstructured and multimodal data , including text, image, PDF, audio, and video.
- Strong understanding of AI/ML operations , particularly evaluation, fine-tuning, and agentic deployments.
GenAI & Quality
- Experience with GenAI data programs, including SFT, preference data, evaluations, and red teaming .
- Strong understanding of quality frameworks, including:
- Rubrics
- Scoring
- Sampling
- Audit
- Adjudication
- Ability to reason about ambiguity, subjectivity, and model behavior.
Nice to Have
- Research background (PhD or equivalent ) in linguistics, STEM, or social science.
- Experience designing or deploying AI/ML solutions in Banking, Financial Services, Insurance, or other regulated enterprise environments .
- Experience working directly with frontier AI labs .
- Experience with agentic systems .