Vetted Power BI Professionals

“Operations-focused leader specializing in rebuilding and scaling marketing/product/commercial operating models during rapid growth and leadership change. Known for implementing integrated quarterly planning, intake/resourcing, and KPI reporting systems that de-silo teams (including B2B enterprise, sales, and marketing) and create predictable execution, clearer decision rights, and scalable operating rhythms.”

“SharePoint specialist with 10+ years delivering end-to-end business process solutions (forms, workflows, pages) and leading on-prem to SharePoint Online migrations. Has hands-on experience building SPFx React + TypeScript dashboards integrated with Nintex workflows, and has solved scale issues in large libraries (50k+ items) by redesigning permission architecture and archiving content. Also partners with product/design on Power BI dashboards, creating layered executive-to-operator UX with drill-through detail.”

Nishitha Thummala

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable inference

San Francisco, CA6y exp

“Backend/retrieval-focused engineer with production experience at Perplexity building a large-scale real-time Q&A system using retrieval-augmented generation, emphasizing low-latency, high-quality answers through ranking, context optimization, and caching. Also has orchestration experience from both product-facing LLM pipelines and large-scale infrastructure workflows at Meta, and has partnered with non-technical stakeholders to align AI trade-offs with business goals.”

Wendy Liu

Screened

Mid-level growth marketing manager specializing in digital strategy and analytics

New Haven, CT4y exp

“Early-career candidate with internship experience spanning podcast/community outreach and B2B SaaS outbound campaigns. They stand out for combining personalized research, webinar-led demand generation, and data-driven email optimization, citing results such as 60% open rates, roughly 100 booked consultations, and a 40% conversion lift from CTA changes.”

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.”

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).”