Vetted NumPy Professionals

Sharan Raj Sivakumar

Screened

Senior Software Developer specializing in AI/ML automation and cloud-native systems

New York City, NY6y exp

“ML/MLOps practitioner who built production systems for telecom network analytics, including an automated labeling + multi-label Random Forest solution that cut labeling effort by 90% and sped up RCA. Led an Ericsson auto-deployment platform using Airflow, Azure IoT Hub, Docker, and Celery to orchestrate 120+ containerized ML/rule-based deployments, saving ~80 hours of setup per deployment.”

Sujith Julakanti

Screened

Junior MLOps Engineer specializing in LLMs and cloud infrastructure

College Station, TX3y exp

“Built a production multimodal LLM system (Gemini on GCP) to automate behavioral coding of family-involved science experiment videos, including preprocessing for inconsistent lighting/audio and LangGraph-orchestrated parallel workflows. Also developed rubric-based AI grading workflows and partnered closely with non-technical education stakeholders through explainability-focused walkthroughs and manual-vs-AI evaluation alignment.”

Connor White

Screened

Junior Machine Learning Engineer specializing in GPU-accelerated computer vision

College Station, TX5y exp

“Robotics software lead from Texas A&M Aggie Robotics who built WoopLib, a SLAM-based vision/navigation library using PID pure pursuit. Has hands-on ROS/ROS2 and Jetson Nano experience integrating Intel RealSense (T265/D435i) with wheel odometry for accurate state estimation, including compiling deprecated sensor support from source and optimizing by moving to Python with C++ bindings and serial streaming to a microcontroller.”

Saicharitha Yanamandala

Screened

Mid-Level Software Developer specializing in Java, Cloud, and Microservices

Chicago, IL6y exp

“Backend/Python engineer who owned an end-to-end FastAPI + AWS internal natural-language document Q&A system (Textract extraction, embeddings/vector DB, LLM integration) with strong focus on reliability and latency. Hands-on with Kubernetes + GitOps (Argo CD, Helm, rolling updates/auto-rollback) and built/optimized Kafka streaming pipelines using Prometheus/Grafana. Also supported a zero-downtime on-prem to cloud migration with parallel run and gradual traffic cutover.”

Arthi R

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and cloud-native microservices

Remote – Washington, D.C.5y exp

“Backend engineer with fintech/banking experience (e.g., Canara Bank) building secure Python/Flask microservices for financial reporting and unified data access. Strong in Postgres/SQLAlchemy performance optimization (including materialized views) and in productionizing ML services on AWS (Lambda/ECS/CloudWatch) with Docker, model registries, and blue-green deployments, plus multi-tenant isolation via JWT-based middleware.”

Kiran M

Screened

Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices and data platforms

Bentonville, AR5y exp

“Backend/ML integration engineer with experience at Accenture and Walmart building Flask-based analytics and prediction APIs on PostgreSQL/MySQL. Strong focus on performance and scalability—uses precomputed aggregates, Redis caching, query tuning (indexes/partitioning/EXPLAIN), and async/background processing; also designs secure multi-tenant isolation with JWT and schema/db-per-tenant strategies.”

Nikhitha santhoshi Maroju

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring microservices on AWS

Redwood, CA4y exp

“Built and shipped a production LLM-powered fraud investigation agent using RAG to generate transaction explanations and draft analyst reports. Emphasizes production robustness (fallbacks, strict structured outputs, async orchestration, monitoring/evals) and reports measurable impact: ~12% precision lift and ~80 high-priority alerts per week with reduced manual effort.”

“Analytics professional with Bank of America experience analyzing the digital card replacement journey at scale, transforming 20M event records across 900K clients into actionable reporting and recommendations. Stands out for combining SQL/Python data engineering, customer journey analytics, and executive-facing storytelling strong enough to get senior leadership to adopt most of their proposed app improvements.”

“Analytics-focused candidate with experience owning end-to-end data projects across AI transcription, retail forecasting, and transportation revenue analytics. They combine strong SQL/Python pipeline skills with dashboarding and stakeholder alignment, citing measurable impact including 60% lower ETL latency, 18% better forecast accuracy, and 25% operational efficiency gains.”

“Healthcare-focused data analyst with hands-on experience at Molina Healthcare building SQL and Python workflows for retention and churn analytics. They combined enrollment, CRM, and claims data into Power BI reporting, automated predictive churn analysis, and tied their work to measurable outcomes including faster processing, better reporting accuracy, and reduced churn.”

Yuchen Wang

Screened

Intern Software Engineer specializing in full-stack development and AI/ML

New York, NY1y exp

“Built and maintains an AI Finance Tracker end-to-end as a solo full-stack product owner, from Figma designs and React frontend to Flask APIs, Firestore, auth, deployment, and AI insights. Stands out for combining product instinct with pragmatic engineering decisions like pre-aggregating financial data to control LLM costs and adding OCR receipt scanning based on real user feedback.”

“ML/AI engineer focused on taking LLM products from experiment to production, with hands-on ownership of a RAG-based customer support system that improved response quality by 35% and cut latency by 30%. Stands out for combining product impact with production rigor across retrieval tuning, safety guardrails, monitoring, and reusable Python/FastAPI services that accelerated adoption across teams.”

“Built and deployed an agentic RAG assistant in production to automate enterprise knowledge search and multi-step workflows with tool calling, tackling real-world issues like hallucinations, retrieval accuracy, and latency. Demonstrates strong LLMOps and orchestration depth (MLflow, Airflow, LangGraph/LangChain/LlamaIndex) plus a metrics-driven approach to agent testing/evaluation and cross-functional delivery with business stakeholders.”

Sriram Degala

Screened

Senior AI/ML Engineer specializing in healthcare and finance AI

Dallas, TX4y exp

“Built production-grade medical AI systems at MD Anderson, including an end-to-end RAG chatbot used by clinical researchers for real-time drug interaction and trial literature queries. Stands out for combining healthcare domain knowledge with strong MLOps, evaluation, and safety practices, and for delivering measurable gains in latency, retrieval precision, and team adoption.”

“Healthcare analytics candidate with hands-on experience turning messy claims and clinical data into validated SQL/Python pipelines and Power BI dashboards. They have delivered measurable impact in revenue cycle operations, including 15-18% improvement in reimbursement accuracy and 40-45% reduction in manual reporting effort.”

“Applied NLP/ML engineer with experience at KeyBank and Novartis building production document intelligence and entity-resolution systems in finance and healthcare. Has delivered end-to-end pipelines (Airflow + AWS) using transformers (DistilBERT/Sentence-BERT), vector search (FAISS/Milvus/Pinecone), and human-in-the-loop labeling to achieve measurable gains (40%+ faster queries; up to 88% F1 and 93% precision/90% recall in entity linking).”

Janvitha Mandyam

Screened

Mid-level AI/ML Engineer specializing in Generative AI and NLP

Chicago, IL4y exp

“GenAI/LLMOps practitioner who deployed a production RAG-based customer service and knowledge retrieval system for a global bank using LangChain, FAISS/Azure Cognitive Search, GPT-4/Claude, and Guardrails—driving a reported 35% Q&A accuracy lift while reducing handle time and escalations. Also partnered with non-technical leaders at CVS Health to deliver ML-driven supply chain risk and inventory insights via anomaly detection, NLG summaries, and stakeholder-friendly dashboards.”

“Backend/data engineer with hands-on production experience building FastAPI APIs secured with JWT and delivering AWS-based data processing solutions using Lambda and ECS Fargate. Has worked with Snowflake/third-party API sources and targets including DynamoDB, S3, RDS, Redshift, and Glue, and uses CloudWatch/X-Ray for monitoring and troubleshooting. Seeking ~$65/hr and is open to onsite work in Bethesda, MD.”

“GenAI/RAG engineer who has deployed a production policy/regulatory search assistant for a financial client using LangChain + Vertex AI, FastAPI, Docker/Kubernetes, and Airflow-orchestrated data pipelines. Demonstrated measurable impact with 50–60% latency reduction and 70% fewer pipeline failures, plus KPI-driven grounding evaluation (90%+ target) and strong cross-functional collaboration with compliance/business teams.”

“Built an end-to-end GenAI underwriting copilot at TD Bank for complex financial documents, combining RoBERTa-based risk classification with Azure OpenAI RAG to deliver grounded, citation-based insights. Drove a 40-50% reduction in manual underwriting review time and created reusable FastAPI ML services that cut integration effort for other teams by 30-40%.”