Vetted scikit-learn Professionals

Aditya Jhaveri

Screened

Mid-level Software Engineer specializing in AI, big data, and distributed systems

Jersey City, NJ3y exp

“Software Developer at NYU (GEMSS) focused on scaling and optimizing a data-heavy asset management web app, including migrating/optimizing data access via Google Sheets API and Firestore. Previously an SDE at Sainapse working on Spring Boot microservices POCs (Kafka, Hadoop at 2B+ record scale). Built an end-to-end Apple Wallet coupon generation/redemption system using PassKit + Google Apps Script with measurable ops impact (40% efficiency gain).”

Sanjana Duvva

Screened

Mid-level AI/ML Engineer specializing in Generative AI, LLMOps, and MLOps

5y exp

“Built and deployed an AWS-based LLM/RAG ticket triage and knowledge retrieval system (Pinecone/FAISS + Step Functions + MLflow) that cut support resolution time by 20%. Demonstrates strong production focus on hallucination reduction, PII security, and low-latency orchestration, with measurable evaluation improvements (e.g., ~25% grounding accuracy gain via re-ranking) and proven collaboration with support operations stakeholders.”

Manasa Mangipudi

Screened

Mid-level Machine Learning Engineer specializing in NLP and computer vision

3y exp

“AI/ML engineer with production experience building an LLM-powered resume-to-job matching and feedback product using RAG, with a strong focus on latency, hallucination control, and scalable deployment. Experienced orchestrating ML inference and backend services on Kubernetes and applying rigorous evaluation/guardrail practices; also partnered with business/product stakeholders at Walmart to improve an NLP-based supplier support system.”

Sri Harshitha Yannam

Screened

Junior Software Engineer specializing in AI/ML and cloud platforms

Austin, TX2y exp

“LLM/agent engineer who shipped a production "Memory Assistant" at HydroX AI, building a LangChain/LlamaIndex RAG memory pipeline on ChromaDB/FAISS with robust fallbacks (BERT/BART), prompt-injection mitigation, and 99.9% uptime monitoring. Also built a multi-step customer support agent using Rasa + OpenAI Assistants API with structured tool calling, guardrails, and human-in-the-loop escalation, and has experience hardening agents against messy ERP data via Pydantic validation, idempotency, and transactional outbox patterns.”

“Data platform engineer focused on large-scale, real-time fraud systems, with hands-on ownership of streaming architectures using Kafka, Spark, Snowflake, and Databricks. Stands out for combining performance tuning and platform automation with LLM/RAG-based enrichment, delivering measurable gains in latency, fraud accuracy, false positives, and analyst decision speed.”

“Analytics candidate with hands-on experience at AT&T building SQL/Python pipelines for churn, usage, billing, and network-performance data at multi-million-row scale. Stands out for combining strong data quality and reconciliation practices with measurable operational impact, including a 30% query runtime improvement and ~8 hours/week of reporting automation savings.”

Harrishkumar Loganathan

Screened

Mid AI/Machine Learning Engineer specializing in FinTech and Generative AI

Remote, USA3y exp

“AI/ML engineer with hands-on ownership of enterprise LLM deployments at Freshworks, including a large-scale RAG chatbot serving 15,000+ users across six departments. Stands out for combining deep production engineering skills—AWS microservices, Kubernetes, observability, retrieval quality, and faithfulness evaluation—with strong cross-functional stakeholder leadership and prior large-scale fraud data pipeline experience at Socure.”

“Built an end-to-end AI resume tailoring system in Python/FastAPI with strong emphasis on reliability, validation, observability, and regression-based quality control. Also demonstrates thoughtful browser and workflow automation skills, especially around brittle UIs, resumable state, and turning ambiguous manual processes into production-ready automated pipelines.”

Saisureshreddy Challa

Screened

Mid-level Data Scientist specializing in AI/ML, LLMs, and domain analytics

California, USA6y exp

“BlackRock AI/ML engineer who built and owned a production LLM document intelligence system for regulatory and investment analysis end-to-end. They combined RAG, multi-agent validation, strong evaluation/monitoring, and reusable Python services to process 50K+ documents, cut review time 40-50%, and improve decision accuracy by about 25%.”

“ML/AI engineer with hands-on ownership of production GenAI and computer vision systems, spanning experimentation, deployment, monitoring, and iterative optimization. Stands out for shipping an enterprise RAG platform that cut manual review by 50% and a defect detection pipeline that reduced report generation from 15 minutes to under 1 second while maintaining high uptime and strong operational discipline.”

“ML/AI engineer with hands-on ownership of production computer vision, speech, and legal RAG systems. Notably improved a key-duplication CV pipeline enough to unblock commercial launch and remove specialist manual measurement, and also shipped a live Quran recitation detection feature for a product with 1M+ users.”

Thomas Le

Screened

Junior Full-Stack Software Developer specializing in web, mobile, and health tech

Remote2y exp

“Developer who uses AI as a productivity accelerator while maintaining strong ownership of code quality, security, and readability. Built an AI-powered planning tool during the PolyPrompt hackathon that transformed messy project requirements into structured tasks, timelines, and assignments, and has also led human teams through Veggie Rescue with a focus on user-aligned execution.”

“Candidate takes a disciplined, developer-in-the-loop approach to AI-assisted coding, using AI primarily for brainstorming, suggestions, and optimization while retaining full ownership of architecture and final code decisions. They also actively stay current on AI developments through research papers, communities, and emerging tools.”

“LLM/agent engineer who shipped a two-stage AI recruitment screening platform at Foursquare that automated resume ingestion through behavioral assessment, delivering an 85% reduction in screening time across 5,000+ applications with auditability and confidence-gated decisions. Also built a multi-agent benchmarking framework using MCP tool interfaces and a RAGAS + LangSmith evaluation/observability stack, including async re-architecture that cut production latency by 50%.”

“Engineering leader with hands-on AI/ML systems experience spanning production inference infrastructure and consumer-facing LLM products. At Jio, they led a 17-person AI features team and delivered measurable execution gains, including 40% faster deployments and 35% lower prediction latency, while also building an end-to-end RAG-based meal recommendation product using OpenAI and Gemini.”

Wei-Hsien Wang

Screened

Entry-level AI Engineer specializing in full-stack generative AI systems

San Jose, CA1y exp

“AI/full-stack product engineer who has shipped both user-facing and internal LLM products, from a photo-to-music recommendation app to an experimentation agent at Azazie. Stands out for combining modern app development with production-grade agent and GraphRAG systems, including a 500k+ email analysis platform and measurable impact like 3x experiment velocity, 75% setup-time reduction, and 65% faster task discovery.”

“UPenn robotics researcher who architected a real-time autonomous driving decision-making engine, integrating LSTM trajectory prediction with MPC in CARLA and adding conformal prediction to deliver 95% statistical safety guarantees under strict latency constraints. Also built and debugged an autonomous quadrotor stack with ESKF-based 6-DoF tracking and optimized A*/Dijkstra planning to eliminate latency-induced instability, with experience bridging heterogeneous simulation/control systems.”

“Autonomous robotics student who built an end-to-end ROS2 semantic goal navigation system as a solo course project, integrating CLIP-based vision-language understanding with SLAM Toolbox and Nav2 to execute natural-language commands in Gazebo/RViz. Also implemented and tuned an RRT planner from scratch in Python and uses Docker plus GitHub workflows for reproducible, tested robotics codebases.”

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“Built and deployed an enterprise GenAI knowledge assistant over thousands of internal PDFs/reports using a RAG stack (GPT-4 + Hugging Face embeddings + vector DB) to reduce manual search and SME escalations. Uses LangGraph/LangChain to orchestrate modular agent workflows with relevance filtering and fallback handling, and applies rigorous evaluation (golden datasets, edge cases, A/B tests) with production monitoring metrics.”

“AI/LLM engineer with production experience at Charles Schwab building a RAG-based assistant to help 5,000+ reps answer complex financial policy questions. Implemented a multi-layer anti-hallucination approach (GNN-driven ontology/graph retrieval + citation-only answers) and compliance-focused guardrails (Azure AI Content Safety) in partnership with audit/compliance stakeholders.”

“Interned at Zonda where he built an AI-driven semantic search solution over ~280M housing/builder records. Iterated from local LLMs via llama.cpp quantization to a vector-embedding retrieval system, then boosted semantic accuracy with a custom spaCy NER layer and re-ranking, optimizing for latency through precomputation. Collaborated with economics-focused stakeholders to reduce manual document/paperwork time by enabling natural-language search over internal data.”

“Built an LLM multi-agent “ingredient safety” analyzer for cosmetics that cuts consumer research time from ~20+ minutes to minutes, using LangGraph orchestration, hybrid retrieval (Qdrant + Tavily), and safety-focused critic validation (false rejections reduced ~30%→~8%). Also has research-internship experience building computer-vision pipelines to classify emerald color/clarity by translating gem-expert heuristics into quantitative model features.”

“Engineer with banking and industrial/IoT experience who has deployed a payment-processing microservice with zero downtime, handling Protobuf schema evolution and sensitive data migration via dual-write/checksum techniques. Demonstrates strong cross-stack troubleshooting (pinpointed intermittent distributed timeouts to a failing ToR switch port) and customer-facing Python ETL customization using plugin-based parsers and Pydantic validation, plus hands-on monitoring/alerting improvements with operators.”