Lead / Senior QA Engineer – with Temporal and LLM, Langfuse

Job Title: Lead / Senior QA Engineer – Agentic AI Systems WITH Langfuse , Temporal 
100% Remote
Interview Mode:2 Video

6-12 MONTHS CONTRACT

We are looking for a highly skilled QA professional to build and scale a next-generation Agentic AI Quality Engineering function. This role goes beyond traditional QA—focusing on validating autonomous AI systems, designing evaluation frameworks, and ensuring high-quality outputs across multiple AI-driven products.

You will play a critical role in shaping how quality is defined, measured, and improved for agentic systems that operate with minimal human intervention.

Key Responsibilities

1. Agentic QA Strategy & Scaling

• Design and scale an agentic QA model for autonomous AI systems
• Move QA from human-driven validation to AI-led evaluation and continuous quality monitoring
• Establish best practices for testing AI agents across lifecycle stages

2. Product Quality Ownership

Own QA for 3 core AI products:

• AI Contact Center solutions
• AI Chat & Form-based interaction systems
• AI Assistants (autonomous / semi-autonomous agents)
• Define quality benchmarks, SLAs, and success metrics for each product
• Proactively identify quality gaps ahead of customer impact

3. Metrics, Observability & Evaluation

• Define and track performance outputs for agentic systems (accuracy, latency, resolution quality, hallucination rate, etc.)
• Build frameworks for:

• Evals & graders (LLM evaluation pipelines)
• Output scoring and benchmarking
• Continuous feedback loops

• Leverage tools like Langfuse for:

• LLM observability and tracing
• Prompt monitoring and performance analysis
• Debugging agent behavior in production

• Analyze:

• Downstream issues
• Production tickets
• Failure patterns

4. Automation & Testing Frameworks

• Build and scale automation across:

• Regression testing
• Smoke testing
• End-to-end agent workflows

• Develop and maintain Playwright-based automation scripts
• Integrate QA into CI/CD pipelines for continuous validation

5. Agentic Testing & Validation

• Design testing approaches for:

• Multi-step agent workflows
• Context retention and reasoning
• Tool usage by agents

• Work with orchestration frameworks like Temporal to:

• Validate long-running workflows
• Test retries, state transitions, and failure handling in agent pipelines

• Account for non-deterministic behavior in AI systems
• Invest additional effort in agentic validation, recognizing higher complexity vs traditional QA

6. Continuous Improvement & Innovation

• Define frameworks to predict and prevent failures before customer exposure
• Continuously improve QA processes using AI and automation
• Partner with Product, Engineering, and AI teams to improve system quality

Required Skills & Experience

• 5–10+ years in QA / Quality Engineering, with strong automation experience
• Hands-on experience with:

• Test automation tools (Playwright preferred)
• API and system testing

• Strong understanding of:

• AI/ML systems (LLMs, conversational AI preferred)
• Evaluation frameworks and benchmarking

• Experience with:

• Temporal (workflow orchestration, stateful systems testing)
• Langfuse (LLM observability, tracing, and evaluation)

• Experience in:

• Building QA frameworks from scratch
• Working with production data, logs, and issue triaging

Good to Have

• Experience with LLM eval frameworks, prompt testing, or AI red-teaming
• Familiarity with agentic architectures / autonomous systems
• Exposure to observability and analytics platforms

Working Model

• Prefer candidates with EST time zone overlap
• Ability to work closely with global product and engineering teams

What Success Looks Like

• A scalable, automated QA system for agentic products
• Measurable improvement in AI output quality and reliability
• Reduced production issues and faster detection of failures

QA evolving from reactive testing to proactive quality intelligence