Technical Program Operations Lead - AI Engineering

Gramian Consulting; Brasil; Presencial; BRL 900.000 - 1.200.000; Jornada completa

Descripción de la vacante

GramianGramian Consultancy seeks an experienced Technical Program Operations Lead to own production systems for large-scale AI and software-engineering data programs. You will translate research requirements into reliable delivery across quality, throughput, contributor performance, timelines, and cost.

This operations leadership role requires inspecting code, understanding tests, and improving workflows at scale, with responsibility for mentoring program leads and acting as a primary client

Formación

• Proven experience leading complex, multi-stakeholder software programs.
• Strong analytical problem-solving skills; identify bottlenecks and define metrics to improve performance.
• Experience managing distributed teams, contributor networks, or large-scale operations.
• Strong customer-facing communication; manage expectations and risks with clients.
• Ability to read code, understand tests, and assess technical work.
• Working knowledge of Python, TypeScript, Java, or Go.
• Experience using metrics to monitor quality, throughput, and delivery.
• Ability to adapt to evolving research requirements and priorities.

Responsabilidades

• 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.

Conocimientos

Proven experience leading complex,-mul

Analytical problem-solving

Distributed teams

Customer-facing communication

Read and review code

Programming language knowledge

Data-driven decision making

Adaptability

Herramientas

Python

SQL

TypeScript

Java

Go

Descripción del empleo

About GramianGramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.

About The Role

We are looking for an experienced Technical Program Operations Lead to own the production systems behind large-scale AI and software-engineering data programs. You will turn complex research requirements into predictable delivery across quality, throughput, contributor performance, timelines, and cost. Programs may include coding datasets, repository-level tasks, agentic trajectories, reinforcement-learning environments, benchmarks, code reviews, and rubric-based evaluations. This is an operations leadership role with a strong technical bar, requiring the ability to inspect code, understand tests, analyze quality signals, and improve workflows at scale.

Key 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.

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