Vetted Jupyter Notebook Professionals

“ML/Simulation engineer who productionized a Multi-Agent Reinforcement Learning system for 30+ firms at Belt and Road Big Data Company, integrating research code into an enterprise backend via Dockerized deployment and scalable data pipelines on GCP/Vertex AI. Demonstrated strong production debugging by tracing apparent network timeouts to hardware memory exhaustion caused by software state-history garbage collection issues, and built custom reward functions to model complex market dynamics (entry/exit, pricing).”

“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”

“IAM/platform engineer with experience at DocuSign and Siemens who ships production-grade systems end-to-end: built a secure AWS serverless internal employee-profile API (OAuth2/Cognito/WAF) that cut data retrieval from weeks to near-instant and sustained ~2,800 RPS at ~75 ms. Also delivered production AI workflows, including a GPT-4o + Playwright crypto-scam detection agent and an NLP ticket-routing system improved to ~86.7% accuracy with strong monitoring and incident mitigation practices.”

“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”

YAKKALI PAVAN

Screened

Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems

USA6y exp

“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”