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Vetted scikit-learn Professionals
Pre-screened and vetted.
Jani Miya Shaik
Mid-level Data Scientist / ML Engineer specializing in FinTech and Healthcare ML systems
“AI/LLM engineer who has shipped production RAG systems (including a 250K-document compliance knowledge tool on AWS) and focuses on reliability via citations, guardrails, and rigorous evaluation (Ragas/Opik/DeepEval). Also built a LangGraph-orchestrated webcrawler agent that cut research paper extraction from hours to minutes, and collaborated with clinical teams to deliver patient volume forecasting with an optimization layer for staffing.”
Fatemeh Taghvaei
Junior AI/ML Engineer and Instructor specializing in deep learning, computer vision, and NLP
“Computer-vision practitioner and educator who built a real-time license plate recognition system (OpenCV/Python + KNN) optimized to run on a Raspberry Pi with camera integration. Also designs hands-on deep learning coursework, incorporating recent transformer-based vision research (Vision Transformers) into practical labs on real datasets.”
Husayn El Sharif
Senior Data Scientist specializing in geospatial ML and environmental analytics
“Applied ML practitioner who deployed a near-real-time water-quality monitoring tool for Gwinnett County by fusing ESA satellite imagery with in-situ measurements to predict chlorophyll-A and support early warnings for harmful algal blooms. Also working on a multimodal deep-learning project combining skin lesion images with patient tabular/text data (TensorFlow, embeddings) to predict melanoma risk.”
Bhanu Gummadi
Mid-level Backend Software Engineer specializing in cloud-native microservices and FinTech
“Backend-focused engineer with Mastercard experience building and operating high-volume transaction-processing microservices. Has owned customer-facing banking services end-to-end and built an internal on-call analytics tool that centralized logs/metrics with real-time filtering to speed root-cause analysis and reduce incident investigation time.”
Sai Rahul Dasari
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and healthcare RAG systems
“Built and deployed a production clinical claim validation RAG system at GE HealthCare that automated nurses’ patient-history/claims checks, cutting manual review time by ~65%. Designed the full stack (retrieval, embeddings, Pinecone, prompt/verification guardrails, FastAPI backend) with PHI-compliant anonymization via NER and orchestrated pipelines using Airflow, Azure ML Pipelines, and MLflow with drift monitoring.”
Surya Vamshi Sriperambudooru
Mid-level AI Engineer specializing in healthcare claims analytics and RAG copilots
“Built a production "appeals co-pilot" for a healthcare claims appeals team, combining an XGBoost/logistic ranking model with a Python/LangChain RAG stack (FAISS + Mistral 7B) to surface high-probability appeal wins and speed policy-grounded drafting. Emphasizes reliability and trust: hybrid retrieval with metadata routing, citation/eval scripts, guardrails, and an explainability layer that non-technical stakeholders could understand and override.”
DEDEEPYA PALAKURTHI
Mid-level AI/ML Engineer specializing in LLMs, RAG, and enterprise MLOps
“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”
PRAJU KUMAR
Mid-level Full-Stack Developer specializing in Java/Spring microservices and React/Angular
“Full-stack engineer with hands-on production experience building real-time customer-facing features (order tracking + push notifications) across React/React Native and Node/Spring Boot with Postgres/MySQL. Demonstrates strong reliability patterns (transactional outbox, background workers, idempotent webhook ingestion) and has deployed/operated systems on AWS (ECS/Fargate/ALB, CloudWatch, CodePipeline) with structured observability and environment separation.”
Harikiran Jangam
Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG systems
“Backend engineer who built and evolved a PHI-compliant RAG system (FastAPI + LangChain + embeddings/FAISS) for internal document search and summarization, delivering <400ms p95 latency at ~2,500 daily requests and measurable impact (30% faster investigations, +17% retrieval relevance). Demonstrates strong security and rollout discipline (RBAC/RLS/JWT, redaction/audits, shadow mode, dual writes, canaries) and a focus on reducing hallucination risk via grounded guardrails and confidence-based fallbacks.”
Ketan Verma
Junior Applied AI Engineer specializing in data pipelines and ML systems
“Built an end-to-end wafer-data anomaly detection and reporting system at Samsung using PySpark, Random Forest models, SQL, and Grafana to help engineers track faults and take corrective action. Also has strong UX prototyping and validation practices in Figma plus hands-on front-end/full-stack experience (HTML/CSS/TypeScript), including a student project recognized as best design out of 25 teams, and early-stage startup experience pivoting a product based on user interviews into a real-time in-context feedback overlay.”
Ganesh Pittala
Senior Software Engineer specializing in distributed systems and cloud-native platforms
“Backend-leaning full-stack engineer with experience at Walmart, Qualtrics, and American Express, shipping secure partner-facing API platforms and internal monitoring dashboards. Strong in AWS production operations (ECS/Fargate, RDS/Postgres, CloudWatch) plus rigorous testing/security practices, with measurable delivery and performance improvements (35% faster releases; ~30–40% latency reductions).”
Kyle Vo
Junior Software Engineer specializing in web, mobile, and embedded systems
“Software/IoT-focused candidate with startup internship experience building a planner AI service integrating Google Calendar (OAuth/token handling) and connecting AI, backend, frontend, and database. Also has embedded systems + AWS networking troubleshooting experience and has implemented TCP networking projects optimized for throughput and low jitter/latency; collaborates with clients via weekly meetings using Trello/Slack.”
Pranita Agrawal
Mid-level Software Engineer specializing in Java microservices and AWS
“TypeScript backend/full-stack engineer who owned an internal business workflow platform end-to-end in production, including API/data design, relational DB integration, and enterprise integrations. Has hands-on experience operating workflow processing services with Kafka-style event-driven patterns, idempotency, exponential backoff retries, dead-letter queues, and strong observability, plus API design with OpenAPI/Swagger and token-based auth.”
Nithya Nagaraj
Mid-Level Software Engineer specializing in cloud-native microservices and FinTech platforms
“Software developer with hands-on experience refactoring a legacy .NET CMS into a newer API-driven application (ASP.NET MVC, JavaScript/HTML), including dynamic asset migration and resolving team merge conflicts in Bitbucket. Built automated tests with PyTest and used Postman for API validation, and leveraged Splunk for production issue detection; also worked on an end-to-end ticketing/workflow management project with prioritization and verification steps.”
Ashrita Mishra
Mid-level Data Analyst specializing in analytics, ETL, and cloud data platforms
“Data analyst with 4 years of experience spanning banking and retail/marketing analytics. Has hands-on experience building churn analytics pipelines in SQL and Python, optimizing large-query performance, and turning stakeholder-aligned metrics into recurring dashboards and business actions.”
Tejasri Joshi
Mid-level Machine Learning Engineer specializing in Healthcare AI and Generative AI
“Analytics professional with Intuit experience spanning modern data stack work, behavioral segmentation, and applied AI. They built dbt/Snowflake pipelines powering retention and churn dashboards, automated feedback classification with OpenAI/LangChain, and partnered closely with product and marketing teams to turn analytics into onboarding, targeting, and lifecycle messaging decisions.”
YaswithaSai Atluri
Mid-level Data Analyst specializing in BI, analytics automation, and cloud data platforms
“Analytics professional with hands-on experience building SQL/Python pipelines, customer ID mapping logic, and self-serve BI dashboards across marketing/CRM and regulated aviation reporting environments. Particularly strong in turning messy multi-source data into trusted reporting assets, with repeated claims of major efficiency gains, faster decision-making, and high-confidence stakeholder adoption.”
Sagar Sidhwa
Senior AI/ML Engineer specializing in LLMs, MLOps, and predictive analytics
“ML/AI engineer with hands-on experience building production MLOps systems for predictive maintenance and demand forecasting, including deployment, monitoring, and iterative retraining. Also shipped a RAG-based employee onboarding chatbot integrated with ServiceNow APIs and reports business impact of roughly $300k/month in reduced stockout and overstock costs.”
Payal Jadhav
Mid-level Software Engineer specializing in full-stack cloud and backend systems
“Full-stack JavaScript engineer (React/Node/Vue) who has operated like a maintainer by owning an internal component library with Storybook-style examples, documentation, and non-breaking versioning. Demonstrated strong performance engineering on a source code review service—profiling bottlenecks, fixing N+1 queries, adding caching, and trimming payloads to cut latency (e.g., ~100ms to <50ms) while rolling out incremental, test-backed improvements.”
Lokesh Jain
Senior AI/ML Engineer specializing in supply chain and healthcare systems
“Built and deployed AcademiQ Ai, a production LLM-based teaching assistant using GPT/BERT with RAG (LangChain + Pinecone) to handle large student notes and generate adaptive explanations/quizzes. Demonstrated measurable retrieval-quality gains (18% precision improvement, 22% less irrelevant context) by tuning similarity thresholds and chunking based on user satisfaction signals. Also orchestrated terabyte-scale, real-time demand forecasting pipelines using Airflow and Kubeflow on GCP with strong monitoring, shadow deployment, and feedback-loop practices.”
Tarun Boreddy
Junior ML Engineer specializing in Generative AI and cloud-based software development
“Graduate student who has built both full-stack web products and an offline Android AI assistant for visually impaired users. Particularly interesting for roles spanning product engineering and applied AI, with hands-on experience in on-device ML, privacy-sensitive deployment, and making complex AI interactions usable for non-technical users.”
Meet Jhaveri
Mid-level Data Scientist specializing in AI/ML, LLMs, and healthcare analytics
“Built and shipped enterprise AI products including a conversational SQL analytics platform and a production RAG system at Johnson & Johnson. Combines full-stack engineering with LLM systems expertise, and has delivered measurable impact at scale, including 48% lower retrieval latency and 37% better response relevance across 12M+ records.”
SREE SAI BINDU DEVALAM
Mid-level Full-Stack .NET Engineer specializing in AI-integrated enterprise applications
“Full-stack engineer who has owned an operations/reporting dashboard end-to-end, spanning React/TypeScript frontend architecture, ASP.NET Core APIs, and SQL data access. Stands out for combining strong UI performance optimization with pragmatic backend decisions, post-launch monitoring, and 0→1 startup platform building that improved API speed by 35% while supporting 2,000+ transactions per hour.”
Kuwarpreet Singh
Junior ML and Full-Stack Engineer specializing in AI/ML applications
“Master's-trained candidate with an AI/ML specialization who combines hands-on machine learning project work with practical AI-assisted software development. They actively use Claude, ChatGPT, and Claude Code for debugging, API integration, multi-file refactoring, and end-to-end feature scaffolding, while emphasizing validation against official docs before shipping.”