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Vetted Recurrent Neural Networks (RNN) Professionals
Pre-screened and vetted.
Mid-level ML Engineer specializing in production NLP, forecasting, and anomaly detection
Mid-level AI/ML Engineer specializing in NLP, recommender systems, and MLOps in financial services
Mid-level Data Scientist specializing in AI/ML for financial services and retail analytics
Junior AI Engineer specializing in NLP, computer vision, and MLOps
Mid-level Generative AI Engineer specializing in LLM orchestration, RAG, and agentic workflows
Mid-level Data Scientist specializing in GenAI, NLP, and recommendation systems
Mid-level AI/ML Engineer specializing in NLP, MLOps, and scalable ML platforms
Mid-level AI/ML Engineer specializing in LLMs, forecasting, and MLOps deployment
Mid-level AI/ML Engineer specializing in LLMs, NLP, and scalable ML pipelines
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines
Senior Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Data Scientist specializing in financial ML, NLP, and MLOps
Srijan Dokania
Junior Robotics & Machine Learning Engineer specializing in perception, SLAM, and edge AI
“Built and deployed an Azure-based, fine-tuned CLIP visual retrieval system at Staples for a ~300k-item product catalog, improving edge-case recall by 12% by engineering a custom delta-similarity/dynamic-margin loss. Also has robotics experience using ROS2 for sensor/compute orchestration, including GPS-time-synchronized sensor triggering for robot swarms and latency-bounded optical-flow benchmarking for edge deployment.”
Soham Patel
Mid-level Machine Learning Engineer specializing in healthcare NLP and MLOps
“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”
Vamsi Koppala
Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems
“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”
shiva kumar kotha
Mid-level AI/ML Engineer specializing in Generative AI and healthcare data
“Built and deployed a production RAG-based document Q&A system on Azure OpenAI to help business teams search thousands of PDFs/Word files, using Qdrant vector search, MongoDB, and a Flask API. Demonstrates strong production engineering (streaming large-file ingestion, parallel preprocessing, monitoring/retries) plus systematic prompt/embedding/chunking experimentation to improve accuracy and reduce hallucinations, and has hands-on orchestration experience with ADF/Airflow/Databricks/Synapse.”
Anurag Reddy
Mid-level Data Scientist specializing in ML, MLOps, and Generative AI
“ML/NLP engineer who built a RAG-based technical assistant for Caterpillar field engineers, transforming PDF keyword search into intent-based semantic retrieval across manuals, logs, sensor reports, and technician notes. Strong in productionizing data/ML systems (Airflow, PySpark) with rigorous preprocessing, entity resolution, and evaluation—delivering measurable gains in accuracy, relevance, and duplicate reduction.”
PHANINDRA KETHAMUKKALA
Senior GenAI/ML Engineer specializing in LLMs, RAG, and multimodal generative AI
“LLM/RAG engineer with production deployments in highly regulated domains (Frost Bank and GE Healthcare). Built secure, explainable document-grounded Q&A systems using LoRA fine-tuning, strict RAG with confidence thresholds, and citation-based responses; also established evaluation/monitoring (golden QA sets, hallucination tracking, drift) and achieved ~40% latency reduction through retrieval/prompt tuning.”