Vetted Machine Learning Engineers

“Full-stack AI engineer who has built and deployed multiple end-to-end LLM products, including an AI interview assistant, a multi-agent market research platform, and a policy document explainer. Particularly strong in productionizing agentic workflows, integrating tools like Whisper, Tavus, LiveKit, CrewAI, and LangGraph, and hardening messy real-world AI/document pipelines with validation, memory isolation, and fallback handling.”

“Built and scaled an internal AI code-search/assistant agent that expanded from engineering-only to broader internal users, tackling legacy code and inconsistent standards to make a RAG pipeline production-ready. Uses a metrics-driven approach (user feedback + automated Python evaluation for retrieval relevance and latency) and has handled high-pressure outages, including moving parts of the stack off AWS and adopting Milvus on internal infrastructure for resilience.”

Muhammad Murtaza Murtaza

Screened

Mid-level Machine Learning Engineer specializing in NLP, Computer Vision & Predictive Analytics

Islamabad, Pakistan5y exp

“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”

“Built and deployed an LLM-powered automation pipeline that ingests voice and documents, transcribes/extracts key information into structured data, and routes it through backend workflows using Python/FastAPI. Uses n8n to orchestrate multi-step AI processes with validation, retries, and monitoring, and iterates with stakeholders via rapid demos to refine changing requirements.”

Muhammad Midhat

Screened

Senior Full-Stack/Backend Engineer specializing in APIs, distributed systems, and AI integrations

9y exp

“AI/backend engineer who has built and scaled production LLM-powered SaaS features (document assistant + compliance review agent) on a Node.js/TypeScript + Postgres/Redis stack deployed to GCP Kubernetes. Demonstrates strong production reliability chops—async queueing, autoscaling, observability, and database tuning—with quantified wins (p95 latency -60%, query 4s to <200ms) and robust AI guardrails (strict RAG, schema validation, citations, HITL).”

“Backend/data engineer with hands-on production experience building a FastAPI Python service on AWS for real-time AI workflows (Postgres/Redis, containers behind API Gateway) with strong reliability practices (JWT auth, timeouts/retries, health checks). Has delivered AWS infrastructure using Terraform + GitHub Actions across environments, built Glue ETL pipelines into Snowflake with idempotent recovery, and modernized legacy batch workflows via parallel-run parity validation and phased cutovers.”

“AI engineer with hands-on experience shipping production systems across semantic search, RAG/LLM applications, and computer vision. Built a personalized e-commerce search platform with measurable relevance and latency gains, and deployed grounded GenAI chat systems that significantly reduced hallucinations while lowering support burden. Also brings edge-deployment experience in monocular depth estimation and 3D reconstruction, suggesting strong breadth across modern applied AI.”