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Vetted Time Series Forecasting Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in NLP, deep learning, and Generative AI
Mid-level Software Engineer specializing in distributed systems and AI agents
Mid-level Data Engineer specializing in real-time streaming and AI/ML data platforms
Senior Supply Chain Specialist specializing in inventory optimization and procurement analytics
Senior GenAI Engineer specializing in enterprise LLM systems and RAG platforms
Mid-level Data Scientist / ML Engineer specializing in risk, fraud, NLP and recommender systems
Mid-level Machine Learning Engineer specializing in NLP, time-series forecasting, and edge AI
Mid-level Data Scientist / AI/ML Engineer specializing in financial services and GenAI
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in NLP, fraud detection, and LLM applications
Mid-level Data Scientist specializing in ML, NLP, and scalable data pipelines
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Bharath Reddy Nallu
Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps
“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”
Laxminarayana Yaga
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”
Rishabhh Garg
Software Engineer specializing in full-stack development and AI/ML automation
“Backend Python engineer focused on production-grade automation and reliability, with hands-on experience designing scalable API systems on PostgreSQL and making pragmatic architecture calls (modular monolith over premature microservices). Demonstrated measurable performance wins (50–60% latency reduction) and strong operational rigor via observability, incremental rollouts/feature flags, and security patterns like JWT + RBAC + database row-level security.”