Taimoor Saqib

– Analyzed task completion logs and identified that annotators performed full evaluations even when label outcomes were predictable from prompt metadata alone. Proposed, designed, and built an agentic pre-labeling system using LangChain and LangGraph that auto-populated structured RLHF fields (quality ratings, category tags, error classifications, function-call correctness scores) before tasks reached human reviewers, shifting the workflow from label-from-scratch to verify-and-correct. – Architected the LangGraph workflow as a multi-node DAG – task parsing → domain-specific analysis → struc-tured field generation → confidence scoring → human routing. Conditional edges bypassed expensive LLM nodes for simple task types using deterministic rule-based pre-filling. – Implemented task-type-specific analysis chains: code generation tasks ran AST parsing and static analysis; function-calling tasks validated schema conformance and argument types; open-ended tasks used rubric-decomposition prompting to pre-score along 4 dimensions (helpfulness, accuracy, harmlessness, instruction-following). – Built confidence-gated routing using annotator override rate as the trust signal - gradually widened the auto-approve threshold as override rates dropped. High-trust task types routed to single-reviewer verification; low-trust types went through full multi-annotator labeling. Reduced total annotator hours by 3x as thresholds stabilized. – Shipped an auto-escalation feature that detected annotator disagreement on submission when labels diverged beyond a configurable threshold, tasks auto-routed to a senior reviewer with a side-by-side diff of each annotator's ratings and the pre-label's prediction. – Built and maintained core backend features on the labeling platform (React, FastAPI, PostgreSQL) serving 2,000+ annotators – including task assignment queue, annotator performance dashboards, and real-time throughput monitoring across 8 programming languages. – Designed automated data validation pipelines in Python and SQL that caught mislabeled samples, duplicate prompt hashes, and label distribution drift - reducing downstream contamination rates from ~12% to ~4%. – Optimized API performance under peak concurrent load using Redis-based session caching and connection pooling, bringing p95 response times from 1.2s to 280ms. – Led a team of 6 engineers across labeling platform and pre-labeler workstreams, owning sprint planning, archi-tectural decisions, code reviews, and production on-call rotation. – Stack: Python, TypeScript, React, FastAPI, SQLAlchemy, LangChain, LangGraph, PostgreSQL, Redis, Docker, AWS (EC2, S3), OpenAI API, CI/CD

– Designed and operated 100+ scheduled ETL jobs on AWS (Lambda, Glue, EventBridge) ingesting lead-generation data from Google LSA and Facebook Ads APIs into a centralized PostgreSQL warehouse. – Built a serverless ingestion pipeline using AWS Lambda and S3 event triggers that replaced a manual CSV upload workflow, reducing data freshness lag from 24 hours to under 15 minutes for real estate lead analytics. – Wrote PySpark transformations to normalize and deduplicate multi-source ad performance datasets (varying schemas, inconsistent date formats, duplicate attribution), producing unified reporting tables serving 6 client dashboards. – Implemented pipeline monitoring using CloudWatch alarms and SNS notifications with automated retry logic, achieving 99.9% job success rate across daily and monthly runs. – Built Tableau and Pentaho dashboards for cross-functional client teams, including a lead attribution funnel track-ing cost-per-lead across channels and surfacing underperforming campaigns. – Stack: Python, PySpark, Pandas, SQL, PostgreSQL, AWS (S3, Lambda, Glue, EventBridge, Athena, EC2, VPC), Tableau, Pentaho

– Served as primary technical contact for overseas financial services clients, performing root-cause analysis on production defects in payment processing systems built on Informix and MySQL. – Optimized slow-running database queries on legacy Informix systems by rewriting joins, adding covering indexes, and restructuring stored procedures — reducing report generation times from 45+ minutes to under 8 minutes for end-of-day settlement reports. – Built an automated SLA tracking and escalation system using SQL queries and Linux cron jobs that generated daily progress reports and auto-escalated breached tickets to senior management. – Documented internal product APIs and common failure modes, creating a troubleshooting runbook that reduced average Tier-1 support resolution time by 20%. – Managed production Linux servers (RHEL/CentOS) executing patch deployments, database maintenance win-dows, and log rotation for systems serving international banking clients. – Stack: Python, SQL, Informix, MySQL, Linux (RHEL/CentOS), Shell Scripting, Git