Project Role : AI Infrastructure Architect
Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.
Must have skills : Large Language Models (LLMs)
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
Build the evaluation and quality engineering capability for AI and agentic systems. Establish measurable quality gates that determine whether systems are safe, reliable, effective, and ready for production.
Expert use of LangSmith, Braintrust, Arize Phoenix, Weights & Biases Weave, MLflow and OpenAI Evals, supported by Codex, Claude Code or Cursor, to build automated regression suites, trajectory evaluations, red-team tests, trace analysis and CI/CD quality gates for production agents.
Must have built evaluation or quality systems for production AI, ML, search, or decision systems. Manual prompt testing and subjective review alone are insufficient.
Roles & Responsibilities:
- Design evaluation strategies for agent behavior, task completion, retrieval quality, groundedness, safety, and reliability.
- Build automated evaluation harnesses, regression suites, benchmark datasets, and production quality gates.
- Evaluate multi-step agent trajectories, tool use, planning, recovery, and human escalation.
- Combine deterministic tests, model-based evaluation, human review, and production telemetry.
- Perform failure analysis, red teaming, and root-cause investigation.
- Integrate evaluations into CI/CD, release, monitoring, and incident-management processes.
- Define scorecards for engineering, risk, product, and business stakeholders.
Professional & Technical Skills:
- Python, test automation, data analysis, statistics, and experimentation.
- LLM and RAG evaluation, agent trajectory analysis, benchmark design, and error taxonomy.
- Tracing, observability, adversarial testing, safety testing, and production monitoring.
- Distinguishing model, retrieval, prompt, tool, data, and orchestration failures.15 years full time education
Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.
Must have skills : Large Language Models (LLMs)
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
Build the evaluation and quality engineering capability for AI and agentic systems. Establish measurable quality gates that determine whether systems are safe, reliable, effective, and ready for production.
Expert use of LangSmith, Braintrust, Arize Phoenix, Weights & Biases Weave, MLflow and OpenAI Evals, supported by Codex, Claude Code or Cursor, to build automated regression suites, trajectory evaluations, red-team tests, trace analysis and CI/CD quality gates for production agents.
Must have built evaluation or quality systems for production AI, ML, search, or decision systems. Manual prompt testing and subjective review alone are insufficient.
Roles & Responsibilities:
- Design evaluation strategies for agent behavior, task completion, retrieval quality, groundedness, safety, and reliability.
- Build automated evaluation harnesses, regression suites, benchmark datasets, and production quality gates.
- Evaluate multi-step agent trajectories, tool use, planning, recovery, and human escalation.
- Combine deterministic tests, model-based evaluation, human review, and production telemetry.
- Perform failure analysis, red teaming, and root-cause investigation.
- Integrate evaluations into CI/CD, release, monitoring, and incident-management processes.
- Define scorecards for engineering, risk, product, and business stakeholders.
Professional & Technical Skills:
- Python, test automation, data analysis, statistics, and experimentation.
- LLM and RAG evaluation, agent trajectory analysis, benchmark design, and error taxonomy.
- Tracing, observability, adversarial testing, safety testing, and production monitoring.
- Distinguishing model, retrieval, prompt, tool, data, and orchestration failures.15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
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