AI Infrastructure Architect

Accenture; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Accenture Bengaluru seeks an AI Infrastructure Architect to design and implement custom AI infrastructure and hardware solutions, optimizing performance, power, cost and scalability of the computational stack. You will guide AI infrastructure technology choices and vendor evaluation for full stack integration.

Role involves building evaluation strategies, automated harnesses, and production-grade quality gates, while collaborating across teams to ensure safe, reliable AI systems ready for

Qualifications

• Minimum 5 years of experience in AI infrastructure, evaluation, or ML systems.
• Experience with production-grade AI systems and CI/CD integration.
• Familiarity with vendor evaluation and full stack integration.

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.

Skills

Large Language Models

Python

Test automation

Data analysis

Statistics

Experimentation

LLM evaluation

Agent trajectories

Benchmark design

Error taxonomy

Observability

Production monitoring

Safety testing

Education

15 years full time education

Tools

LangSmith

Braintrust

Arize Phoenix

Weights & Biases Weave

MLflow

OpenAI Evals

Codex

Claude Code

Cursor

Job description

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

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