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Vetted Time Series Forecasting Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in machine learning and analytics
Mid-level Data Analyst/Data Engineer specializing in machine learning and NLP
Junior AI Engineer specializing in distributed ML pipelines and time-series forecasting
Mid-level AI Engineer specializing in retail personalization and LLM-powered systems
Senior Data Analyst specializing in healthcare, insurance, and financial analytics
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”
Yashwanth Penugonda
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production GenAI systems
“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”
Executive CTO and Engineering Leader specializing in AI/ML, computer vision, and scalable systems
Mid-level Machine Learning Engineer specializing in healthcare and enterprise analytics
Mid-level AI/ML Engineer specializing in financial risk, NLP, and MLOps
VamshiKrishna Challa
Mid-level Data Scientist specializing in industrial IoT, predictive analytics, and generative AI
“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”
Mid-level MLOps/ML Engineer specializing in LLMs and financial risk modeling
Senior Data Scientist / AI-ML Engineer specializing in LLMs, NLP, and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Senior Data Scientist and Machine Learning Researcher specializing in NLP, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in Generative AI and RAG assistants
Mid-level Machine Learning Engineer specializing in healthcare and financial AI
Pallavi Yellisetty
Mid-level AI/ML Engineer specializing in predictive modeling, NLP, and recommender systems
“AI/ML manager who has deployed production NLP in healthcare—mining unstructured clinical notes and combining them with structured patient data to predict readmissions, with strong emphasis on data alignment and terminology normalization. Also experienced operationalizing ML with Airflow/MLflow and AWS Step Functions/SageMaker, plus stakeholder-facing Power BI dashboards (e.g., marketing customer segmentation).”
Chien-Ting Hung
Director-level AI Engineer specializing in computer vision and LLM/RAG platforms
“Hands-on LLM/RAG engineer with production experience improving retrieval quality and stability by addressing messy data, vector DB inaccuracy, and top-K issues—ultimately redesigning to hybrid search with tuned keyword/semantic weighting and MCP-based data supplementation. Also brings strong AKS/Kubernetes deployment experience, optimizing CI/CD speed via lightweight local Docker validation and decomposing pods to avoid full rebuilds, plus a metrics-driven approach to agent/workflow testing and traceability.”