Staff Test Engineer - AI
Outreach is hiring a Staff Test Engineer to lead quality for their agentic AI platform. The role involves designing evaluation frameworks for GenAI, agent orchestration, and ML pipelines. You will collaborate with data science and engineering to define metrics and CI/CD standards for non-deterministic AI systems. The position requires strong expertise in Python and distributed system testing at scale.
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Experience
7-12 years
Function
Quality Assurance
Work mode
Onsite, India
Company
Tier 2
What you will work on
Outreach is hiring a Staff Test Engineer to lead quality for their agentic AI platform. The role involves designing evaluation frameworks for GenAI, agent orchestration, and ML pipelines. You will collaborate with data science and engineering to define metrics and CI/CD standards for non-deterministic AI systems. The position requires strong expertise in Python and distributed system testing at scale.
TAL's take
Strong, high-impact staff-level role focused on GenAI quality at an established tech company, though location is regional.
Very clear JD outlining specific responsibilities for AI testing, tools, and cross-functional partnerships.
Must haves
- 7-12 years experience in software development or test automation
- B.S. in Computer Science or related field
- Strong Python programming skills
- Experience testing large-scale backend or platform systems
- Experience with Databricks for ML pipelines
- Experience with MLflow for experiment tracking
Tools and skills
Nice to have: langgraph, langchain, aws, gcp, azure, docker, kubernetes, prompt engineering.
About the company
Established sales execution platform company, though not in the Tier 1 global/India flagship list provided.
Posts mentioning Outreach
Title: Data Scientist (~2 YOE) – Built planning tools, pipelines, and AI system. Need honest feedback on profile.
Hi all, Looking for practical feedback on my profile before I start applying. I’ll keep this structured so it’s easier to evaluate. --- 1) Planning Tools / Web Applications Problem: Forecasting workflows were fragmented and heavily Excel-driven: - Multiple data sources (orders, shipments, different forecast versions) - Manual merging, lookups, and adjustments - No way to simulate scenarios or compare forecasts cleanly - Different planners using different methods → inconsistency What I built: - Two internal applications for planning workflows: - A planning tool integrating 8+ data sources - A forecasting simulator supporting multi-level editing (high → granular) Key capabilities: - Real-time scenario simulation - Side-by-side comparison of multiple forecast types - Hierarchical adjustments across levels - SQL write-back for persistence Scale: - Processes ~150K+ records per cycle - Used in monthly planning cycles by multiple teams Impact: - Removed fragmented Excel workflows - Enabled consistent decision-making across users - Reduced manual effort and improved visibility into forecast behavior --- 2) Automation & Data Pipelines Problem: Core workflows were manual and repetitive: - Multi-file Excel processing - Data cleaning + merging across systems - Version tracking errors - High effort per cycle (1–4 hours depending on workflow) What I built: - Multiple pipelines automating end-to-end workflows Examples: - Large-scale consolidation pipeline: - Input: ~1M+ rows across 20+ files - Output: clean, unified dataset (~75% reduction) - 2nd pipeline: - Replaced a 23-step manual process - Standardized inconsistent formats across datasets - 3rd automation processing: - Automated unpivoting, enrichment, and version tracking Impact: - Reduced processing time from hours → minutes per cycle - Eliminated manual errors (copy-paste, lookup mistakes) - Standardized workflows across users --- 3) Power BI / Monitoring Problem: Recent data (orders/shipments) showed inconsistencies, but: - No visibility into changes over time - Hard to identify where data drift was happening What I built: - Power BI dashboards with: - Hierarchical filters - Drill-down views - Month-over-month comparison Scale: - ~30K+ records analyzed Impact: - Enabled early detection of data inconsistencies - Helped planners validate inputs before forecasting - Improved trust in upstream data --- 4) Side Project (AI System) What I built: - AI-powered job assistant system Features: - Scrapes job postings - Scores relevance using LLMs - Generates tailored resume points and outreach messages - Tracks applications Tech: - FastAPI backend - LLM routing (cloud + local fallback) - SQLite storage Goal: - Build a system-driven workflow (not just model usage) --- My concern Most of my work sits at the intersection of: - forecasting - data systems - workflow automation I’m trying to move into: 👉 Applied Data Scientist / Product-oriented roles --- Questions 1. Does this profile look too niche (forecasting-heavy)? 2. Does “building systems around data” help or hurt for DS roles? 3. What’s the biggest gap you see (if any)? Would really appreciate honest feedback. Thanks.
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