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About the Role

This role involves leading the design, development, and delivery of production-grade AI systems to drive measurable business outcomes. It requires a seasoned engineer with a track record of shipping complex AI systems end-to-end, combining deep technical expertise with strong cross-functional partnership and architectural judgment. The individual will own key workstreams, serve as a technical leader, mentor engineers, partner with business stakeholders, and ensure AI capabilities meet production-grade standards for reliability, scalability, and measurability. This position demands independent judgment, a bias toward delivery, and the ability to translate ambiguous business needs into well-scoped, executed solutions.

Responsibilities

Lead the architecture and delivery of production AI systems, including document intelligence (IDP), intelligent knowledge systems, and agentic orchestration, to power workflow automation at scale.
Own AI projects end-to-end, from problem scoping and stakeholder alignment through solution design, implementation, deployment, monitoring, and continuous improvement.
Drive technical design and architectural decisions for the team, including API design, system decomposition, evaluation strategy, and infrastructure patterns.
Architect and champion robust evaluation frameworks for AI systems, defining statistically sound metrics, curating benchmark datasets, and enforcing strict versioning.
Partner directly with cross-functional stakeholders, including Product, Operations, Legal, and Business teams, to identify AI opportunities, translate requirements, and communicate tradeoffs, risks, and recommendations.
Mentor and develop engineers on the team through code review, design review, pair problem-solving, and knowledge sharing.
Identify systemic problems and propose solutions, proactively improving team processes, tooling, and infrastructure.

Requirements

7+ years of experience developing production AI/ML systems.
Hands-on experience with AWS or cloud-native development patterns for AI/ML workloads.
Demonstrated track record of delivering complex systems from inception through production.
Strong proficiency in Python and ability to build well-engineered, maintainable software.
Adherence to software engineering best practices (source control, CI/CD, testing, documentation).
Deep expertise in at least one of the following: LLM-based systems (fine-tuning, inference optimization, prompt engineering, modern tooling AI tooling, such as transformers, vLLM, or agentic frameworks), document intelligence and IDP, or ML system design (training pipelines, model serving, evaluation infrastructure).
Experience designing and operating end-to-end ML pipelines in production, including model training, deployment, monitoring, and iteration (MLOps).
Solid fundamentals in statistics, experimentation, and data quality with the ability to reason rigorously about metrics, error patterns, and the limitations of AI systems.
Experience leading technical design, mentoring engineers, and driving architectural decisions within a team.
Strong written and verbal communication skills to represent the team in cross-functional settings, document technical designs, and communicate effectively with both technical and non-technical stakeholders.
Experience spanning more than one of LLM systems, document intelligence, and ML platform and infrastructure.
Familiarity with agentic architectures and protocols (i.e. MCP and A2A) or designing multi-step, tool-using AI workflows.
Knowledge of cost and latency optimization for LLM inference at scale (i.e. quantization, batching strategies, and model routing).
Prior experience in financial services or FinTech, particularly in document-heavy or compliance-sensitive domains.
Contributions to open-source projects or published technical writing demonstrating thought leadership in applied AI.

Skills

Python
Software engineering best practices (source control, CI/CD, testing, documentation)
LLM-based systems (fine-tuning, inference optimization, prompt engineering, modern tooling, agentic frameworks)
Document intelligence (IDP)
ML system design (training pipelines, model serving, evaluation infrastructure)
MLOps
Statistics
Experimentation
Data quality
Technical design leadership
Mentoring
Architectural decision-making
Written communication
Verbal communication
Agentic architectures and protocols (MCP, A2A)
Multi-step, tool-using AI workflow design
Cost and latency optimization for LLM inference (quantization, batching strategies, model routing)
AWS
Cloud-native development patterns for AI/ML workloads

Location

Onsite (Monday-Thursday)
Remote (Friday)

Work Type

Full-time
Hybrid

Experience Level

7+ years of experience

Salary/Compensations

Base salary: $170,000 - $200,000
Compensation package includes salary, equity for all full-time employees, and an annual performance bonus

Benefits

Equity for full-time employees
Annual performance bonus
Employer matched retirement plan
Generously subsidized healthcare
100% employer paid dental
100% employer paid vision
Telemedicine
Virtual mental health counseling
Parental leave
Unlimited paid time off (PTO)

Equal Opportunity

iCapital is an Equal Employment Opportunity and Affirmative Action employer.
Does not discriminate based upon race, religion, color, national origin, gender, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.