Vetted Power BI Professionals

“MBA-trained operator-investor who interned in venture capital at Marquee Ventures while also founding GoodBites Brisket Jerky. Brings a rare combination of startup evaluation experience and true zero-to-one execution, having built outbound sales, sourced manufacturers, created a website with Claude Code, and run day-to-day operations for a specialty food brand.”

Nikhil Reddy

Screened

Mid-level AI/ML Engineer specializing in GPU inference and LLM platforms

San Francisco, CA5y exp

“Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.”

Krishna Reddy

Screened

Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants

New York, NY6y exp

“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”

“Project/program manager with enterprise software implementation experience spanning Dynamics CE/CRM, Azure cloud migrations, SAP modules, and Project Online. Notably led a 20-person cross-functional team on a multi-country CRM migration across the US, UK, India, and Canada while simultaneously managing countries in requirements, pre-go-live, and post-go-live stabilization phases.”

Shuvam Mitra

Screened

Mid-level Data Scientist specializing in anomaly detection and production ML

Pittsburgh, PA4y exp

“Interned at Backblaze building production AI systems for incident response and security operations, including an internal LLM-powered incident triage assistant that used Snowflake + RAG over historical tickets/postmortems and delivered results via Slack and a web UI. Emphasizes reliability (PII filtering, grounding, schema validation, fallbacks) and rigorous evaluation/observability (offline replay, partial rollouts, time-to-first-action metrics, Prometheus/Grafana).”

“ML/AI engineer with hands-on ownership of production computer vision and GenAI systems, spanning real-time public safety video analytics and RAG-based knowledge assistants. Stands out for translating research-oriented approaches into scalable, monitored production systems with clear business impact, including 50% latency reductions, 25% faster response times, and 40% lower document search time.”

“ML/LLM engineer who built a production RAG system (GPT-4 + FAISS + FastAPI) to deliver fast, grounded answers from proprietary documents, optimizing for sub-200ms latency and high-concurrency scale. Strong MLOps/observability background: drift monitoring with Prometheus + Streamlit, automated retraining via Airflow, Kubernetes autoscaling, and MLflow-managed model lifecycle, plus inference cost reduction through quantization and structured pruning.”

“Talent/Recruiting Operations leader with large-scale, multi-region experience at Amazon and Home Depot, specializing in standardizing hiring workflows across 30+ sites. Known for building robust SOP/SLA frameworks and Power BI analytics (40+ dashboards) that drove a 30% time-to-fill reduction and cut agency reliance by 25%, plus leading enterprise ATS/tool integrations with cross-functional partners (IT, Security, Legal, Finance).”