Technical Program Operations Lead - AI Engineering

Gramian Consulting Group

About The Position

Gramian Consultancy is seeking an experienced Technical Program Operations Lead to manage the production systems for large-scale AI and software-engineering data programs. This role involves translating complex research requirements into predictable delivery across various metrics including quality, throughput, contributor performance, timelines, and cost. The programs can encompass tasks such as coding datasets, repository-level operations, agentic trajectories, reinforcement-learning environments, benchmarks, code reviews, and rubric-based evaluations. It is an operations leadership position with a high technical standard, requiring the ability to review code, understand tests, analyze quality signals, and enhance workflows at scale.

Requirements

Proven experience leading complex, multi-stakeholder programs in software engineering, technical program management, consulting, finance, startups, operations, or a similar environment.
Strong analytical and problem-solving skills, including the ability to identify bottlenecks, define meaningful metrics, and improve production performance.
Experience managing distributed teams, contributor networks, marketplaces, or large-scale technical operations.
Strong customer-facing communication skills, including managing expectations, communicating risks, and building long-term client relationships.
Ability to read and review code, understand test suites, and independently assess technical work.
Working knowledge of at least one programming language such as Python, TypeScript, Java, or Go.
Experience using data and operational metrics to monitor quality, throughput, performance, and delivery.
Ability to operate effectively in environments where research requirements and priorities evolve quickly.

Responsibilities

Own end-to-end program delivery across scope, timelines, quality, throughput, contributor performance, and cost.
Design and manage workflows for coding datasets, agentic trajectories, RL environments, benchmarks, and rubric-based evaluations.
Identify operational bottlenecks and improve workflows through better instructions, sequencing, incentives, review systems, and capacity planning.
Define contributor requirements and partner with talent teams to source, assess, onboard, train, and ramp distributed software engineers.
Build team-lead and reviewer structures for programs involving 100–1,000+ contributors.
Own quality-control systems and analyze datasets to identify trends, systematic errors, and root causes.
Act as a primary customer contact for AI labs, communicating progress, risks, quality trends, and recovery plans.
Translate research objectives into practical task specifications and challenge requirements when they may not produce the intended evaluation signal.
Use Python, SQL, or similar tools to automate quality sampling, defect analysis, throughput reporting, and operational reviews.
Convert successful workflows into reusable playbooks, quality controls, evaluation assets, and contributor-management systems.
Share operational learnings and mentor other program leads.