Vetted Power BI Professionals

“Outbound-focused sales candidate with experience spanning SaaS-style prospecting at ShineCo and customer-facing consultative selling at Madrasi Chai. Stands out for highly personalized, insight-led outreach into higher education and wellness organizations, including generating 10+ qualified leads per month and turning cold, initially resistant stakeholders into meetings through research-backed messaging.”

“Backend-leaning engineer who has significantly owned the architecture behind complex browser-based internal analytics dashboards for enterprise operations teams. Stands out for connecting Python/FastAPI/Django backend design, async processing, PostgreSQL optimization, and browser performance improvements to make real-time monitoring UIs faster and more usable.”

“Analytics-oriented candidate with hands-on GTM and sales operations experience in financial services plus applied project leadership at Northeastern. Built reporting systems in Power BI/Tableau, used Salesforce for client segmentation and campaign tracking, and created reusable launch-management tools adopted by multiple teams.”

“Performance-focused paid social creative lead with deep Meta experience in DTC/eCommerce for a bridal brand, owning strategy, briefs, testing, and QA while delegating production. Known for rapid iteration (8–12 variants/week) and measurable lifts (2.8 ROAS; CPA down 25%+), plus strong UGC direction and landing-page alignment that materially improves conversion.”

“Backend engineer with research and industry experience building data-intensive systems for healthcare and IoT. Built Python/Flask/FastAPI services with real-time ingestion and ETL into relational databases, emphasizing data quality, performance tuning, and secure access controls (JWT, RBAC, row-level filtering). Notably caught hardware-driven sensor anomalies others missed and implemented quarantine/alerting to prevent bad data from corrupting analytics.”

“AI/LLM engineer who has shipped production RAG chatbots using LangChain/OpenAI with FAISS and FastAPI, focusing on real-world constraints like context windows, concurrency, and latency (reported ~40% latency reduction and <2s average response). Experienced orchestrating AI pipelines with Celery and fault-tolerant long-running workflows with Temporal, and has applied NLP model tradeoff testing (Word2Vec vs BERT) to drive measurable accuracy gains.”

“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”

“ML/computer-vision practitioner who shipped a CycleGAN-based bilingual handwriting translation demo (English↔Telugu) for low-resource scripts using unpaired datasets, focusing on preserving handwriting style and real-time deployment via Gradio. Also delivered a medical imaging pipeline by fine-tuning ResNet-50 and ViT-B/16 for pneumonia detection, emphasizing reproducibility, measurable evaluation, and stakeholder-friendly iteration.”

“Built a production-style LLM document summarization/generation workflow that mitigates token limits and reduces hallucinations using semantic chunking, FAISS-based embedding retrieval (top-k via cosine similarity), and section-wise generation. Orchestrated the end-to-end pipeline with AWS Step Functions and aligned outputs with sales stakeholders through demos, visuals, and documentation.”

“LLM/RAG practitioner who has taken a support-ticket triage automation system from prototype to production, building the full pipeline (fine-tuned models, FastAPI inference services, vector storage, monitoring) and delivering measurable impact (~40% reduction in triage time). Demonstrates strong operational troubleshooting of LLM/agentic workflows (observability-driven debugging, fixing agent routing/looping) and supports adoption through tailored demos and sales-aligned technical communication.”

“Applied AI/ML engineer with hands-on production experience building a RAG-based AI assistant for pharmaceutical maintenance troubleshooting using LangChain + FAISS/Pinecone, including a custom normalization layer to handle inconsistent terminology and duplicate document revisions. Also built Airflow-orchestrated pipelines for document ingestion/embeddings and predictive maintenance workflows (SCADA ETL, drift-based retraining), and partnered closely with production supervisors/quality engineers via Power BI dashboards and real-time alerts.”

“LLM/RAG engineer who has built and shipped production assistants, including a RAG-based teaching assistant (Marvel AI) using LangChain/LlamaIndex/ChromaDB with OpenAI embeddings and Redis vector search, achieving ~30% accuracy gains and ~35% latency reduction. Also deployed FastAPI services on Google Cloud Run with observability and prompt-level monitoring, and partnered with non-technical ops stakeholders to deliver an internal policy-document RAG assistant.”

“Cross-functional operator/chief-of-staff style leader who took a product from prototype to a live pilot in 3 months, spanning public-sector data normalization, an ML matching engine, a secure API, and KPI/investor demo instrumentation. Strong focus on executive alignment and productivity via Notion-based operating systems plus automated reporting (Python/Power BI), with experience supporting fundraising and go-to-market narratives.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”

“Data engineer who has owned production pipelines end-to-end, ingesting 50–100 GB/day from APIs/S3 and near-real-time Kafka into Snowflake with strong data quality gates (Great Expectations/dbt) and Airflow-based reliability (SLAs, alerting, dashboards). Also built a Snowflake-backed REST data API with caching/pagination and versioned endpoints, and designed a compliant, scalable web-scraping system with anti-bot handling and safe backfills.”

Bhoomika Gururaja

Screened

Intern Product & Project Management professional specializing in analytics-driven delivery

Seattle, WA1y exp

“Product Management Intern who owned an end-to-end sourcing-style initiative for a new digital product launch, coordinating internal stakeholders and external vendors. Uses data-driven, value-focused negotiations and milestone-based delivery management (Jira/Smartsheet) to control scope, timelines, and supplier performance while proactively mitigating cost and schedule risks.”

Harsh Chauhan

Screened

Junior AI Engineer specializing in Generative AI, RAG, and NLP

Remote, US3y exp

“AI/LLM engineer who has shipped a production RAG platform at Ticker Inc. on GCP (Qdrant + Postgres) delivering sub-second retrieval over 550k+ items, with measurable gains in latency and answer quality (HNSW optimization, MMR re-ranking). Also built an asynchronous LangChain/LangGraph multi-agent research system (10x faster cycles) and partnered with Indiana University doctors on synthetic patient records and ML error analysis using clinician-friendly F1/loss dashboards.”

Sebastian Rodriguez

Screened

Junior International Business professional specializing in e-commerce and internationalization

Medellín, Colombia2y exp

“Former university student-group PR director and streamer connected to Colombia’s gaming/creator ecosystem. Led university gaming-related events by packaging and selling social media promotion (Instagram/TikTok) to sponsors and exhibitors, generating ~20M COP (~$7k USD) in sponsorship value and tracking success via attendee turnout and partner sales.”

Mayur Agrawal

Screened

Entry-Level Backend Engineer specializing in analytics automation and cloud data pipelines

Bengaluru, India1y exp

“Forward Deployment Engineer focused on application security and production integrations, with hands-on experience hardening API-driven ticketing systems (JWT/RBAC/rate limiting/log redaction) and implementing CI/CD security controls (Bandit SAST, SCA, container hardening). Strong in diagnosing peak-load production issues using logs/metrics/infra signals and driving durable fixes like adaptive throttling and backoff, while aligning engineering, business, and leadership stakeholders on risk and SLA impact.”

Dawood Shaik

Screened

Senior Product Marketing Manager specializing in GTM, channel enablement, and pricing analytics

Minneapolis, MN9y exp

“Growth-creative/performance creative specialist focused on UGC-led paid social across Meta, TikTok, and YouTube. Uses structured creative testing (e.g., hook/angle matrices) and modular asset systems to iterate quickly; cited a campaign that lifted efficiency (25% CPA improvement, ROAS 1.8x→2.4x) and then scaled spend ~60% while maintaining performance.”

“Analytics professional with hands-on experience turning messy Excel-based operational data into SQL/Python pipelines and Power BI dashboards, including a production bottleneck project that improved workflow efficiency by 20%. Also brings applied machine learning experience from a Databricks/PySpark loan risk scoring project using logistic regression and XGBoost on large-scale S3 data.”

“Analytics candidate with hands-on experience turning messy retail/customer data into clean reporting tables using SQL and PostgreSQL, then extending the work into Python-based reusable analysis workflows. They have applied segmentation, cohort analysis, and retention metric design to support dashboards and improve targeting, engagement, and repeat purchase performance.”

Srinivasan Gomadam Ramesh

Screened

Mid-level AI/Data Engineer specializing in agentic AI and data platforms

Redmond, WA7y exp

“AI/LLM engineer who built a production resume-parsing and candidate-matching platform at Quadrant Technologies, combining agentic LangChain workflows, VLM-based document template extraction (~85% accuracy), and a hybrid RAG backend for resume-to-JD search. Notably integrated automated LLM evals and metric-based CI/CD quality gates to catch silent prompt/model regressions, and led a 3-person team across frontend/backend/testing.”