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Vetted Python Professionals
Pre-screened and vetted.
Vamshi Arempula
Senior AI/ML Engineer specializing in Generative AI, RAG, and agentic systems
“GenAI/LLM ML engineer (currently at Webprobo) building an enterprise GenAI platform with document intelligence and automation on AWS and blockchain. Has hands-on experience with RAG, LLM evaluation tooling, and orchestrating production LLM workflows with Apache Airflow, plus deep exposure to reliability challenges in globally distributed/edge deployments. Also partnered with business/marketing stakeholders at a banking client to deliver an AI-driven customer retention insights solution.”
Shashank Garg
Engineering leader specializing in FinTech ML/AI platforms
“Engineering Manager/player-coach leading Data Infrastructure, ML/DS, and AI Engineering pods who recently shipped multiple production agentic GenAI features. Built privacy-preserving LLM workflows (PII redaction via Microsoft Presidio) and drove an AI expense-approval agent from ambiguous ask to GA, cutting approval time from ~2.5 days to <4 hours with >85% accuracy. Also owned a major LLM cost overrun incident and implemented cost observability plus circuit breakers to prevent runaway agent loops.”
Sanket Mungikar
Mid-level Full-Stack Developer specializing in AI-powered analytics platforms
“Backend/DevOps engineer pivoting into robotics/space, building hands-on ROS2 (Humble) skills via Gazebo simulations and experimenting with Nav2 and slam_toolbox. Brings strong distributed-systems and real-time debugging practices (profiling, instrumentation, QoS/retry patterns) and is actively learning perception and control fundamentals to transition into autonomous robotics.”
Murali Marupudi
Mid-Level Full-Stack Software Developer specializing in cloud-native microservices
“Backend engineer focused on high-throughput Python/Flask systems on AWS, with strong scaling and performance tuning experience (e.g., PostgreSQL join reduced from ~3s to <200ms; background aggregation cut from 10 minutes to <90 seconds with 8x throughput). Has also integrated ML model serving into production APIs (churn prediction) using Celery/Redis batching and AWS Lambda/S3, and designed secure multi-tenant architectures with PostgreSQL schema isolation and row-level security.”
Ajay Desai
Mid-Level Full-Stack Software Engineer specializing in FinTech and platform APIs
“Backend/AI engineer with experience in both high-scale financial services (JP Morgan trade compliance analytics API on Java/Spring Boot/Postgres/Elasticsearch on AWS EKS processing 1M+ trades/day) and applied LLM systems for legal research (LangChain/OpenAI + Weaviate semantic search). Demonstrated strength in reliability/performance engineering, data consistency during migrations, and production-grade workflow orchestration with observability and human-in-the-loop guardrails.”
Sai Kondapaka
Mid-Level Software Engineer specializing in microservices, data pipelines, and FinTech fraud detection
“Backend engineer with PayPal experience owning a high-throughput, low-latency fraud detection pipeline processing billions of transactions/day, integrating LLM-based models into real-time Kafka streams and payment decisioning APIs. Strong Kubernetes + GitOps practitioner (declarative, auditable deployments; autoscaling/probe tuning) with migration experience modernizing legacy systems onto AKS/OpenShift.”
Mayur Komaravelly
Senior Data Analyst specializing in data pipelines, web scraping, and legal data enrichment
“Data engineer focused on reliable, scalable analytics pipelines and external data collection. Has owned end-to-end pipelines processing 5–10M records/day, serving Snowflake data marts to Power BI/Tableau, and reports ~99% reliability through strong validation/monitoring. Also shipped versioned REST APIs for curated data with query optimization and caching.”
Ansh Harjai
Junior Software Engineer specializing in AI, RAG systems, and backend development
“Built an NYU software engineering capstone called “Smart Cash AI,” a multi-agent LLM-powered web app that curates offline-ready podcasts/articles/videos/news based on user preferences and commute schedules. Architected agent orchestration (discovery/downloader/summarizer), real-time progress via WebSockets, and an ETL normalization layer across RSS/YouTube and other sources with GUID-based deduplication, retries, and failure isolation to keep the system predictable.”
Razim Julianto
Senior QA Engineer specializing in manual/automation testing for web and mobile products
“QA tester with experience at Tokopedia (major Indonesian e-commerce) handling concurrent web/Android/iOS releases, including regression testing, stakeholder coordination, and full bug-ticket lifecycle management. Has additional PC/mobile game testing exposure (personal/adhoc) and uses AI tools to generate edge test cases and detect bug patterns; interested in taking on heavy, high-impact projects.”
Harsha Golla
Mid-level Supply Chain Analyst specializing in inventory optimization and demand planning
“Wayfair strategic sourcing/procurement professional who led a major shift from push-based replenishment to a demand-driven pull model, combining SAP IBP/S4HANA planning with supplier renegotiations and performance scorecards. Drove measurable outcomes including $100K annual savings, 98% on-time delivery, and an SAP Fiori + Tableau inventory visibility improvement that cut 1,500 units of waste and saved $150K.”
Dhruv Pandoh
Junior Full-Stack Software Engineer specializing in AI and FinTech platforms
“Built and shipped a production RAG-based assistant for ROS2 developer support that ingests ~1GB/day from docs, GitHub, and YouTube, using Qdrant vector search plus LoRA-tuned LLaMA generation. Emphasizes production reliability with explicit orchestration, structured outputs, monitoring (ClearML), and guardrails/fallbacks to prevent hallucinations and degrade gracefully when retrieval is weak.”
Vadivelan Murugesan
Junior Robotics Engineer specializing in ROS 2, perception, and motion planning
“Robotics software engineer/researcher (master’s work) who built a human-aware motion planning stack for a UR16/UR16e arm: RGB-D 3D skeleton perception in ROS2, deep-learning-based human motion prediction, and MoveIt2-integrated real-time planning with a Gazebo digital twin. Demonstrated strong real-time optimization (profiling + GPU offload with CuPy/TensorRT) and practical systems skills spanning safety validation, visualization, and low-level comms (CAN/SocketCAN) on embedded deployments (Jetson, Docker, Autoware/Ouster).”
Sampada shelke
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and applied research
“New grad SDE (AI/ML) who built and deployed an LLM-based chatbot framework used across technology, military, and banking contexts, focusing on model selection tradeoffs (latency vs accuracy) through prototyping and benchmarking. Also built a multi-agent "eaterybot" using PyAutoGen/AutoGen with a manager agent orchestrating specialized agents, and emphasizes rigorous testing with adversarial/edge-case datasets and hallucination checks.”
Chris Colinsky
Executive Technology Leader/CTO specializing in data platforms, AI agents, and e-commerce/payments
“Engineering leader with hands-on coding time who has driven major commerce and data-platform transformations: defined goop’s omnichannel strategy, unified payments to Square, and rebuilt real-time NetSuite inventory flows plus forecasting tools. Currently reorganized engineering into Product/Data/Support teams to hit aggressive seasonal roadmaps, and led a data-lake/medallion ELT refactor feeding embedded analytics (Tinybird) with improved reliability and cost efficiency; also accelerates onboarding via AI coding tools in a serverless, event-driven architecture.”
SNEHA KUSUMA
Mid-level Java Full-Stack Developer specializing in banking and telecom platforms
“Frontend-focused engineer with experience at T-Mobile and U.S. Bank who maintained a TypeScript utility library (types, tests, build pipeline, and docs) adopted by multiple teams, and improved React workflow performance by refactoring components and optimizing data fetching. Known for pragmatic cross-team support—reproducing issues quickly, shipping well-tested fixes, and managing changes carefully to avoid breaking downstream apps.”
Rishitha reddy katamareddy
Mid-level Generative AI & Machine Learning Engineer specializing in agentic LLM systems
“Built and deployed a production agentic LLM knowledge assistant that answers complex questions over internal documents, APIs, and databases using a RAG architecture (FAISS/Pinecone) and LangChain/LangGraph orchestration. Emphasizes production-grade reliability and hallucination control through grounding, confidence thresholds, validation, retries/fallbacks, and full observability (logging/metrics/traces) with continuous evaluation and feedback loops.”
Hamidreza Lotfalizadeh
Mid-level AI/ML Engineer specializing in LLM agents, RAG, and ML systems
“At Inertia Systems, built a production LLM-powered ingestion pipeline that converts heterogeneous sources (PDF/JSON/IFC/SQL and financial tables) into standardized text and uses GraphRAG to construct a knowledge graph with verified dependency relationships. Also has hands-on HPC orchestration experience with SLURM, including creating a custom wrapper process manager to improve resource utilization under restrictive scheduling policies.”
JiHeon Kim
Mid-Level Backend Software Engineer specializing in Financial Services
“Frontend engineer focused on React/TypeScript dashboards and end-to-end product delivery, including RBAC with JWT, Redux-based session persistence, and a centralized API layer with token injection. Has experience with real-time MongoDB-backed updates (shared across web and mobile) and has solved production-impacting issues like timezone inconsistencies using date-fns/UTC conversions, backed by Jest + manual QA and rollback-driven release practices.”
Qichen Zhao
Intern Software Engineer specializing in Applied AI and LLM systems
“Built and deployed a production RAG-based conversational "Yelp for AI tools" at Search-AI Inc., focused on personalized, explainable AI tool recommendations from thousands of options. Emphasizes production-grade reliability and performance (hybrid retrieval, async two-stage pipelines) and is also building a multi-agent orchestration layer (MAgIc) with typed memory and controlled coordination policies.”
Sumit Kothari
Junior AI/Full-Stack Engineer specializing in LLM apps and RAG systems
“AI engineer who built and shipped a production AI document-understanding/search system at Sumeru Inc, including a full RAG + LLMOps evaluation stack (MLflow, DeepEval, RAGAS) deployed on GCP. Also developed LangChain/LangGraph multi-agent workflows for UAV flight-log analysis and has experience presenting AI solutions to non-technical stakeholders and prospect clients to drive POCs.”
Sanket Dumbare
Mid-Level Software Engineer specializing in cloud-native microservices and FinTech platforms
“Backend/platform engineer who led an end-to-end Python (FastAPI) transaction analytics microservice for real-time financial monitoring, including SQS ingestion, scoring/aggregation, and low-latency APIs. Strong AWS + Kubernetes/GitOps background (EKS, ArgoCD, Jenkins, ECS/ECR, CloudWatch) with hands-on experience scaling event-driven systems and executing phased on-prem to AWS migrations.”
Naga Renuka Kandi
Junior Software Engineer specializing in cloud, full-stack development, and Generative AI
“Built and shipped a production Chrome extension (Promptly) that lets users select text on any webpage and transform it in place (rewrite/shorten/translate) using on-device AI plus external LLMs. Implemented a custom lightweight orchestration layer for prompt chaining, context flow, and output validation, and tackled tricky browser Selection API issues to preserve formatting while keeping the UX simple and fast.”
Fangjian Xiong
Junior Machine Learning Engineer specializing in NLP and biomedical entity extraction
“Built and deployed a production LLM-powered biomedical knowledge extraction pipeline that processed millions of papers to identify tools/techniques and produce a unified knowledge graph via active learning NER (Prodigy + spaCy transformers) and entity linking (Bio-tools/Wikidata). Addressed hard NLP engineering challenges like WordPiece span-offset alignment and scaled inference over ~1.5M documents using batching/caching, containerized services, async workers, and orchestration with Prefect/Airflow.”
Daniel Jin
Intern Site Reliability Engineer specializing in Kubernetes, AWS, and observability
“Backend/data engineering candidate specializing in Python/Flask services and ML-enabled systems, deploying containerized workloads on AWS ECS/EKS with strong observability (Prometheus/Grafana) and PostgreSQL performance tuning. Built multi-tenant architectures with row- and schema-level isolation and optimized a Kubernetes-based Airflow + Spark nightly ETL pipeline for an e-commerce client, improving performance by 250%+ and reliably beating morning reporting deadlines; also contributed to Apache Airflow (SQLAlchemy/PostgreSQL area).”