Job description

BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage. The IZOT product line includes BMC’s Intelligent Z Optimization & Transformation products, which help the world’s largest companies to monitor and manage their mainframe systems. The modernization of mainframe is the beating heart of our product line, and we achieve this goal by developing products that improve the developer experience, the mainframe integration, the speed of application development, the quality of the code and the applications’ security, while reducing operational costs and risks. We acquired several companies along the way, and we continue to grow, innovate, and perfect our solutions on an ongoing basis. About the Role You build the systems that tell us whether our agentic AI is good enough to ship: evaluation frameworks, golden datasets, and rubrics; automated eval harnesses in CI; and drive down failure modes such as hallucination, drift, and unsafe or non-repeatable output. You are the reason customers can trust what our agents produce. At this level you independently own well-scoped work from definition through delivery. Scope at this level: Independently owns well-scoped eval work for a feature/team from definition through delivery. Organizational impact expected: Improves outcomes for one team or project. Key Responsibilities Core responsibilities for this role: • Define and implement evaluation strategies and frameworks for Generative AI, RAG, and agentic AI applications. • Create golden datasets, rubrics, and scoring methods (including LLM-as-a-judge) so quality is measured consistently. • Apply statistical sampling, repeated trials, judge calibration, dataset versioning, and anti-contamination practices. • Stand up automated evaluation harnesses in CI so model/prompt/agent changes are re-scored before ship. • Define quality metrics and release gates for customer-ready vs prototype. • Design testing strategies for multi-agent workflows and AI systems using tools, APIs, MCP, and enterprise apps; Playwright where relevant. • Evaluate end-to-end agent behaviour (planning, tool use, response generation, task completion). • Identify failure modes using observability/tracing; recommend reliability improvements. • Close the loop from evaluation results into model/prompt improvements with DS and AI Engineering. • Champion Responsible-AI and human-in-the-loop principles; contribute to governance and model validation. Must-Have Skills & Experience • Bachelor's or master's in computer science, AI, or related field (or equivalent experience). • Experience with Generative AI / LLM applications or AI/ML systems and quality/evaluation frameworks (depth scales with level). • Understanding of LLM/RAG/agentic failure modes; vector DBs and embedding retrieval as level requires. • Python; building eval sets, rubrics, automated scoring; CI/CD integration. • LLM observability/tracing (e.g. Langfuse, OTel) and analytics (OpenSearch or similar) as level requires. • Rigorous, data-driven mindset; collaboration across DS, engineering, and product. • Evidence of owning an eval suite that influenced a release decision. • Builds consistent rubrics/datasets — not one-off manual spot checks only. • Works independently on scoped quality problems. Evidence we will look for in hiring: • Past experience: Owned a defined evaluation suite or quality workflow for an AI feature and partnered with stakeholders on “good enough.” • Delivery evidence: Decision-ready quality report; evals integrated into a release or CI path for one product/team. • Shared expectation: Independently owns well-scoped work from definition through delivery. Nice-to-Have Skills • Ragas, DeepEval, promptfoo, or similar eval frameworks. • AI safety/governance; enterprise/regulated environments. • OpenShift/AWS; performance/load/reliability testing of AI apps. • MLOps; open-weight models/local serving; MCP; Agile/Atlassian Our commitment to you! BMC’s culture is built around its people. We have 6000+ brilliant minds working together across the globe. You won’t be known just by your employee number, but for your true authentic self. BMC lets you be YOU! If after reading the above, You’re unsure if you meet the qualifications of this role but are deeply excited about BMC and this team, we still encourage you to apply! We want to attract talents from diverse backgrounds and experience to ensure we face the world together with the best ideas! BMC is committed to equal opportunity employment regardless of race, age, sex, creed, color, religion, citizenship status, sexual orientation, gender, gender expression, gender identity, national origin, disability, marital status, pregnancy, disabled veteran or status as a protected veteran. If you need a reasonable accommodation for any part of the application and hiring process, visit the accommodation request page. BMC Software maintains a strict policy of not requesting any form of payment in exchange for employment opportunities, upholding a fair and ethical hiring process. The annual base salary range represents the low and high end of the BMC salary range for this position. Actual salaries depend on a wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training, licensure, and certifications; and other business and organizational needs. The range listed is just one component of BMC's employee compensation package. Other rewards may include a variable plan and country specific benefits. At BMC, it is not typical for an individual to be hired at /near the top of the range. A reasonable estimate of the current range is $152,925 - $254,875

Requirements

Must-haves

At this level you independently own well-scoped work from definition through delivery.
Scope at this level: Independently owns well-scoped eval work for a feature/team from definition through delivery.
Organizational impact expected: Improves outcomes for one team or project.
Define and implement evaluation strategies and frameworks for Generative AI, RAG, and agentic AI applications.
Create golden datasets, rubrics, and scoring methods (including LLM-as-a-judge) so quality is measured consistently.
Apply statistical sampling, repeated trials, judge calibration, dataset versioning, and anti-contamination practices.
Stand up automated evaluation harnesses in CI so model/prompt/agent changes are re-scored before ship.
Define quality metrics and release gates for customer-ready vs prototype.
Design testing strategies for multi-agent workflows and AI systems using tools, APIs, MCP, and enterprise apps; Playwright where relevant.
Evaluate end-to-end agent behaviour (planning, tool use, response generation, task completion).
Identify failure modes using observability/tracing; recommend reliability improvements.
Close the loop from evaluation results into model/prompt improvements with DS and AI Engineering.
Champion Responsible-AI and human-in-the-loop principles; contribute to governance and model validation.
Must-Have Skills & Experience
Bachelor's or master's in computer science, AI, or related field (or equivalent experience).
Experience with Generative AI / LLM applications or AI/ML systems and quality/evaluation frameworks (depth scales with level).
Understanding of LLM/RAG/agentic failure modes; vector DBs and embedding retrieval as level requires.
Python; building eval sets, rubrics, automated scoring; CI/CD integration.
LLM observability/tracing (e.g. Langfuse, OTel) and analytics (OpenSearch or similar) as level requires.
Rigorous, data-driven mindset; collaboration across DS, engineering, and product.
Evidence of owning an eval suite that influenced a release decision.
Builds consistent rubrics/datasets — not one-off manual spot checks only.
Works independently on scoped quality problems.

Nice-to-haves

Nice-to-Have Skills

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmlpythonci/cdawsagile

Platform: avature
Effort: medium