Vetted Time Series Forecasting Professionals

“Analytics professional with experience across Dreamline AI, Ultron Technologies, and Infolabz, building SQL/Python data pipelines and BI dashboards for incentive, FMCG, and retail use cases. Stands out for turning messy multi-source data into trusted reporting, automating recurring analytics, and tying dashboard adoption to measurable business outcomes like 50% faster reporting and 30% ROI improvement.”

“Built a production ad-spend optimization system that combined deterministic audit logic with LLM-generated explanations, surfacing severe inefficiencies including 70-90% wasted spend in some Google Ads accounts. Stands out for pairing measurable business impact with pragmatic AI safety and usability decisions, including approval-gated execution and structured, human-readable recommendations.”

“Built GynAI, an end-to-end maternal clinical decision support platform for OB/GYN practices and hospitals in North America, combining predictive ML with RAG-based LLM explainability. The candidate emphasizes real production ownership across experimentation, deployment, monitoring, and iteration, with reported impact including fewer delayed interventions in high-risk pregnancies and a 15-20% reduction in false positives.”

“Robotics competition (ABU Robocon) team member who programmed two robots for a rugby-style game, integrating IoT sensors and real-time decision-making. Implemented low-latency, secure inter-robot communication by moving from Bluetooth to ESP8266/NodeMCU WiFi (with Bluetooth as backup) and used OpenCV plus CNN training workflows for vision-related tasks; no practical ROS/ROS2 experience.”

“Built and shipped a production AI/LLM agent-based, event-driven credit underwriting/decisioning workflow that automated document understanding, retrieval, risk scoring, and compliance checks—cutting turnaround from ~90 days to ~5 minutes while boosting throughput 200x+ and approvals ~50%. Experienced with Airflow/Prefect orchestration, Redis/RabbitMQ queues, rigorous eval/monitoring, and close collaboration with non-technical underwriting teams.”

“Data science professional who has built a production RAG-based LLM question-answering system ("Flash Query") to deliver fast, accurate answers over large document collections, focusing on retrieval quality and grounded responses. Also collaborates with non-technical retail/jewelry stakeholders to turn business questions into predictive models and dashboards for decision-making.”

“Built and deployed real-world systems end-to-end across security and healthcare contexts: led a 3-person team delivering a university vehicle tracking system with 30% cost savings and 1-year post-launch monitoring. Also implemented a healthcare RAG chatbot with adaptive query routing that cut LLM costs by 40% while maintaining answer accuracy, and has experience debugging non-deterministic LLM behavior in DevOps pipeline automation.”

“Data analytics and BI professional with experience turning messy institutional and customer data into decision-ready reporting and predictive systems. They combine strong SQL/Python execution with end-to-end ownership of churn analytics, stakeholder alignment, and operational rollout into dashboards and CRM workflows.”

Visswanath Baskaran

Screened

Mid-level Data Analyst specializing in BI, ETL, and forecasting

Tampa, FL4y exp

“Analytics professional with hands-on experience at Charge Dock building SQL- and Power BI-based operational reporting for SLA compliance, uptime, and incident management. Also developed a reproducible Python demand forecasting workflow using Pandas and Statsmodels to support inventory planning, showing a blend of BI, data engineering, and forecasting capability.”

“Software engineer with a strong builder mindset who has worked across ML, backend, and frontend systems. Notably built an AI-driven predictive autoscaler for Kubernetes from scratch using Prometheus, TensorFlow, Flask, and Spring Boot, and also delivered customer-facing automation features in financial document processing by working directly with auditors to translate domain rules into product logic.”

“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”

“Built and owned a production RAG-based internal knowledge assistant end-to-end, from experimentation through cloud deployment and monitoring. Demonstrated strong practical GenAI judgment by choosing prompt optimization and retrieval tuning over fine-tuning for dynamic data, driving a 40% to 50% reduction in time to answer while improving relevance, lowering hallucinations, and increasing productivity.”

“Built an end-to-end agentic population health strategy copilot for healthcare leadership, turning broad chronic disease questions into structured, evidence-backed strategy briefs. Stands out for combining healthcare domain knowledge with production-grade GenAI implementation, including LangGraph orchestration, Databricks/MLflow deployment, human review, and quality gates focused on citations, metrics, risks, and safety.”

chaitra putumbaka

Screened

Junior Data Scientist and Software Engineer specializing in ML and cloud applications

Leesburg, VA2y exp

“Backend engineer currently at Intuit Mailchimp working on transactional messaging infrastructure, with hands-on experience improving backlog-count latency through database redesign and sharding. Also has practical experience integrating Mailchimp capabilities into MCP-based AI workflows and previously worked on a financial-domain LLM chatbot using prompt design and RAG.”

“Built and deployed production LLM applications including a natural-language-to-read-only-SQL system focused on ambiguity handling and query safety (schema whitelisting, intent validation, confidence checks, deterministic execution). Experienced with LangChain-based, modular agent orchestration and RAG document QA for large PDFs, with a metrics-driven testing/evaluation approach and cross-functional delivery with marketing on an AI content recommendation/search tool.”

HarshaVardhanRao Vemuganti

Screened

Junior AI/ML Engineer specializing in Generative and Agentic AI

Lafayette, Louisiana4y exp

“Built and deployed a production-grade LLM agent for credit management and accounts receivable automation, integrating ERP/MySQL data via a RAG pipeline and exposing services through FastAPI with Pydantic-validated outputs on AWS Bedrock. Emphasizes reliability and compliance for financial operations using schema validation and human-in-the-loop review, reporting ~32% reduction in manual work and ~41% improvement in response time/reliability.”

Faisal Hayat

Screened

Senior Python Full-Stack Engineer specializing in AI-powered backend systems

Lahore, Pakistan6y exp

“Backend-leaning full-stack engineer with startup experience building AI-powered products and ERP/SaaS platforms. They’ve delivered Python/FastAPI/Django and React systems on AWS, including an AI document processing platform and business workflow tools used by thousands of users, with strong hands-on depth in database optimization and production operations.”