Vetted Data Validation Professionals

“Backend/platform engineer who has owned real-time reporting and streaming analytics systems end-to-end, combining FastAPI/Postgres APIs with Kafka consumers, Celery background jobs, and Redis caching. Strong DevOps/GitOps experience deploying Python/Node microservices to AWS EKS with Helm, ArgoCD/FluxCD, and CI pipelines, and has supported phased on-prem to AWS migrations using Terraform and traffic cutovers.”

Christopher Sipola

Screened

Senior Machine Learning Engineer specializing in LLMs and recommendation systems

New York, NY11y exp

“ML/GenAI engineer who owned major parts of Spotify’s AI DJ from offline experimentation through deployment, monitoring, and iteration. They combine recommender systems, RAG, real-time feedback loops, and LLM safety/orchestration to ship consumer-facing personalization features that drove double-digit engagement and deeper listening sessions.”

“Built and deployed a production LLM conversational AI system at OpenAI supporting chat, summarization, and semantic search at 1M+ requests/day, driving major latency (40%) and accuracy (25%) improvements through Pinecone optimization and tighter RAG with re-ranking. Also has Amazon experience improving recommendation systems by translating ML metrics into business terms to boost CTR and conversions, with strong MLOps/orchestration depth (Airflow, MLflow, SageMaker, Kubeflow).”

Gabriel Aguiar

Screened

Entry-Level Software Engineer specializing in AWS cloud infrastructure and distributed systems

Arlington, VA1y exp

“Robotics software engineer with hands-on ROS 2 experience who helped build an autonomous 5-DOF robotic arm that plays Backgammon, owning perception (OpenCV) and game-logic while adding robustness features like lighting tolerance and auto-calibration. Also worked on a Raspberry Pi/LiDAR car project, improving mapping accuracy through data-logged calibration and contributing to multi-robot collision-avoidance coordination via a server-based pub/sub system.”

“Research-oriented ML engineer/scientist with deep experience applying generative models, adaptive optimization, and HPC infrastructure to complex physics analyses. Built reusable Python-based tools that replaced expensive Monte Carlo workflows, integrated across HTCondor/SLURM environments, and reduced analysis timelines by 2x while supporting broader team adoption and training.”

“LLM/agentic systems practitioner who partners directly with customers to productionize prototypes end-to-end—defining business-aligned metrics, building evaluation datasets, and shipping monitored, cost-bounded inference APIs on AWS Lambda. Notably delivered a vehicle damage classification system that cut manual review by 40% and stabilized agent workflows by instrumenting state transitions to uncover and fix a race-condition-driven skipped tool call.”

“ML/NLP engineer with experience at NVIDIA and Cruise building production-grade AI systems across genomics/biomedical research and autonomous vehicle data. Has delivered multimodal LLM pipelines, large-scale entity resolution, and hybrid semantic search (BERT embeddings + FAISS + Elasticsearch), with measurable impact (≈40% accuracy/retrieval gains; ≈30% data consistency improvement) and strong MLOps practices (Kubernetes, CI/CD, MLflow, Prometheus/Grafana).”

Sri Yogesh Dorbala

Screened

Engineering Manager / Tech Lead specializing in large-scale distributed systems

Seattle, WA8y exp

“Software engineer focused on personalization and data/ML infrastructure who built a GenAI/LLM-driven carousel ranking system end-to-end, delivering a reported 6–7% order-rate lift. Also designed large-scale personalization ETL (15PB for ~100M users) and created a custom Airflow operator to integrate with Databricks under enterprise version constraints, with hands-on on-call and data-quality reliability improvements.”