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Vetted SciPy Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in Python backends and cloud web applications
Senior AI/ML Data Scientist specializing in cloud-deployed NLP, CV, and MLOps
Mid-level AI/ML Engineer specializing in scalable ML systems and cloud MLOps
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and deep learning
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and MLOps
Mid-level Data Engineer specializing in cloud data pipelines and analytics platforms
Mid-level Data Scientist specializing in customer analytics, ML pipelines, and churn forecasting
Mid-level Backend Software Engineer specializing in microservices and cloud APIs
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and MLOps
Mid-level AI/ML Engineer specializing in NLP, RAG, and agentic AI
Junior Multimodal AI & Systems Engineer specializing in robotics and cloud infrastructure
Mid-level Data Engineer specializing in AWS, Spark, and streaming data pipelines
Senior AI/ML Engineer specializing in GenAI, LLMs, NLP, and MLOps
JYOTHI N
Senior Data Scientist specializing in analytics, experimentation, and BI on AWS
“Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.”
Siliang Zhang
Intern Machine Learning Engineer specializing in LLMs, RAG, and vision-language systems
“Robotics ML/software engineer focused on Vision-Language-Action control for 7-DoF robots, replacing tokenized action decoding with continuous regression heads (including a logit-weighted expectation approach) to improve stability and real-time behavior. Strong in ROS1/ROS2 systems integration and debugging closed-loop manipulation issues via latency instrumentation, QoS-aware distributed messaging, and sim-to-real validation using Gazebo/Unity, Docker, and CI pipelines.”
Haider Shah
Principal AI/ML Architect specializing in GenAI, LLMs, RAG, and Agentic AI
“FinTech/AI engineer who has shipped an end-to-end discrepancy-detection product for financial managers using Next.js, FastAPI/GraphQL, Pinecone, and AWS (with dev/staging/prod, observability, A/B testing, and documentation). Also built an AI-native “AI Genesis” system with agentic cyclic workflows, routing, and tool use, and has experience modernizing legacy systems via the strangler fig pattern while coordinating with senior stakeholders on a 5G autonomous simulation platform.”
Ryan McDowell
Senior Software Engineer specializing in pricing, marketplaces, and data engineering
“Built and operationalized intelligent pricing infrastructure for live event ticketing at StubHub, emphasizing iterative prototyping with traders and production-grade monitoring (Splunk, API/data-stream thresholding). Also partnered with customer-facing teams to drive adoption and helped win a significant consignment revenue-share deal by demoing the system to the Philadelphia 76ers and quantifying pricing efficacy and business impact.”
Nanditha Umasankar
Mid-level Robotics Software Engineer specializing in autonomy, ROS2, and SLAM
“Robotics software engineer leading an autonomy stack migration from ROS1 to ROS2, including a custom-built global parameter server to preserve existing infrastructure while shipping continuous production releases. Hands-on across navigation/safety/monitoring packages, control (ROS2 PID for steering/speed), and localization performance work (particle filter optimization), with strong ownership of CI-driven test strategy and release quality.”
David Kidwell
Senior AI/ML Data Scientist specializing in NLP, computer vision, and MLOps
“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”