Technical Program Operations Lead - AI Engineering
New
G
Gramian ConsultancyAI engineering
Workable workplace: remote; Workable locations: United States. Bangladesh. Brazil. Colombia. Egypt. GhanaContractLead
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLJavaTypeScriptGo
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 identifying bottlenecks, defining meaningful metrics, and improving 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 as 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, reinforcement-learning environments, benchmarks, and rubric-based evaluations.
- Identify bottlenecks and improve workflows through 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 for 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 task specifications and challenge requirements that 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 learnings and mentor other program leads.
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