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Vetted Python Professionals
Pre-screened and vetted.
Weston Carpenter
Mid-level Backend Software Engineer specializing in distributed systems
“Technical/presales engineer with experience at Grubhub and Appen, spanning LLM-adjacent data labeling workflows and production AI troubleshooting. Built an integrations platform at Grubhub and has hands-on experience diagnosing prompt-related AI failures via Splunk, adding JUnit tests and logging to prevent recurrence. Known for shipping customer-specific workflow adaptations (e.g., OCR annotation coordinate transformations for crop/rotation) while keeping timelines intact through iterative delivery and parallelization.”
Sharan Raj Sivakumar
Senior Software Developer specializing in AI/ML automation and cloud-native systems
“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.”
Madhav Vaddepalli
Senior Data Engineer specializing in cloud data platforms and big data pipelines
“Data engineer focused on building reliable, production-grade pipelines and external data collection systems on AWS (S3/Lambda/SQS/Glue/EMR) using PySpark/SQL, serving curated datasets to Snowflake/Redshift for finance and fraud teams. Has operated a large-scale crawler ingesting millions of records/day with anti-bot tactics, schema versioning/quarantine, and CloudWatch/Datadog monitoring, and also shipped a versioned REST API with caching and query optimization.”
Aayush Anand
Intern Full-Stack/Software Engineer specializing in web apps, cloud, and data/ML systems
“Built and productionized LLM-driven content intelligence/SEO agents for a high-traffic media platform, automating tagging/summarization/metadata with FastAPI + async orchestration and strict JSON-schema outputs. Demonstrated measurable impact (40% faster publishing, +20% organic traffic in 3 months) and strong reliability practices (offline evals, shadow mode, canaries, fallbacks, idempotency, and monitoring).”
vinay maruthi
Mid-level Software Engineer specializing in LLM agents and ERP-integrated workflow automation
“Built and shipped a production LLM-powered agent that automated purchasing and inventory operations by integrating with live ERP data and returning structured, machine-readable outputs usable by downstream systems. Emphasizes real-world reliability through orchestration, strict schemas/validation, confidence-based fallbacks with human handoff, and monitoring/evaluation feedback loops to reduce silent failures and make issues observable.”
Sushanth Reddy
Mid-level Data Engineer specializing in cloud ETL/ELT and big data pipelines
“Data engineer focused on production-grade pipelines and data services: ingests millions of records/day into S3, performs SQL/Python quality validation and PySpark/SQL transformations, and serves curated datasets via Athena/Redshift. Has experience hardening external data collection with retries/rate-limit handling and shipping versioned internal data APIs with backward compatibility, monitoring, and CI/CD in early-stage environments.”
Nivedita Shainaj Nair
Mid-level ML Data Engineer specializing in MLOps and scalable healthcare data pipelines
“Data/ML platform engineer with healthcare (Cigna) experience owning an end-to-end pipeline spanning Airflow + Debezium CDC ingestion, PySpark/SQL transformations, rigorous data quality gates, and feature-store/API serving for ML training and inference. Worked at 10+ TB scale and cites a ~30% latency reduction plus stronger reliability via idempotent design, monitoring, and backfill-safe reprocessing; also built pragmatic early-stage data pipelines at Frankenbuild Ventures.”
Shiva Ganduru
Senior Backend Software Engineer specializing in microservices, Kafka, and cloud-native AWS platforms
“LLM/agent engineer with production experience in the insurance claims domain, integrating OpenAI + LangChain into a claims platform to automate unstructured document extraction/classification and cut manual effort by 35%. Built reliable, fault-tolerant AWS/Kubernetes microservices with CloudWatch monitoring plus circuit breakers/retries/fallbacks, and implemented multi-step Spring Boot orchestration with schema validation, confidence gating, and human-in-the-loop handling for low-confidence cases.”
Aswani D
Mid-level Software Engineer specializing in cloud microservices and data pipelines
“Data engineer/platform builder who has owned production pipelines end-to-end processing millions of records/day, with strong emphasis on data quality (quarantine workflows) and reliability (monitoring, retries, incremental loads). Also designed large-scale external data collection/crawling with anti-bot handling and backfills, and shipped versioned REST data services optimized for performance and developer usability in an early-stage environment.”
Matthew Penner
Intern Software Engineer specializing in systems programming and web development
“Entry-level candidate who built an air-traffic-control (ATC) simulation game in Rust, using a tick-based loop to model real-time behavior. No hands-on robotics/ROS experience yet, but has foundational software development tooling experience with GitHub and Linux/Vim.”
Ashwini Ramesh Kumar
Junior AI Software Engineer specializing in LLMs, RAG, and agent workflows
“Backend/ML-leaning engineer who built a content-based event recommender for FlowMingle using embeddings + HNSW vector search on Google Cloud, with Firebase as the backend and a managed recommendation lifecycle (15 recs/user, daily async generation, weekly deletion) now serving 1500+ users. Also led a cost-driven migration of ConvAI services to Azure AI using parallel request testing from a Unity client, with post-migration monitoring via logs and model evals; contributed to a Massachusetts law-enforcement conversation analysis system by expanding ingestion to PDF/TXT/Excel and multi-file inputs.”
Shivam Goel
Senior Robotics Researcher specializing in neurosymbolic robot learning and manipulation
“Robotics software researcher who led a Boston Dynamics SPOT project on non-prehensile manipulation of heavy boxes, combining MuJoCo-based RL, ViT-based perception, and SPOT SDK control; the work is under review for ICRA 2026. Also built a ROS planning-and-learning stack on a LoCoBot using PDDL task planning, RTAB-Map SLAM, MoveIt motion planning, and RL to recover from execution failures.”
Connor White
Junior Machine Learning Engineer specializing in GPU-accelerated computer vision
“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.”
Sujith Julakanti
Junior MLOps Engineer specializing in LLMs and cloud infrastructure
“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.”
Keerthi Kalluri
Senior Full-Stack & GenAI Engineer specializing in healthcare and financial services
“Built and deployed a production LLM-powered customer support assistant using a RAG backend in Python, focused on deflecting repetitive Tier-1 tickets and reducing resolution time. Demonstrates strong production engineering instincts around reliability (confidence scoring + human fallback), scalability/cost optimization (multi-stage pipelines), and workflow orchestration/observability (LangChain, custom DAGs, structured logging, step metrics).”
Shweta Gupta
Senior Backend Software Engineer specializing in Java microservices, Kafka, and AWS
“AI engineer who shipped a production chat assistant for a storage company by building the underlying RAG-style knowledge base (document ingestion, chunking/embeddings, FAISS vector store) and an admin update interface to keep content current. Also has full-stack delivery experience (Python REST APIs + React/TypeScript UI) and AWS operations using Terraform/Jenkins, including handling a real production performance incident by optimizing DB queries and adding auto-scaling.”
Alex Johnson
Mid-Level Full-Stack Software Engineer specializing in observability and developer tools
“Product-leaning full-stack engineer (65% product / 35% infra) who built core components of the LightFoot feature flag platform: end-to-end client/server SDKs with OpenTelemetry-based observability and a React+TypeScript UI for flag management and metrics dashboards. Strong focus on performance (memoization/lazy loading/caching), reliable API design, and Postgres modeling for read-heavy flag evaluation workloads, with AWS production experience (EC2/ECS/Lambda/API Gateway/VPC).”
Akhilesh Babu Tumati
Mid-level Full-Stack Software Engineer specializing in cloud-native and AI-integrated systems
“Built and deployed a Virginia Tech CS department blog/archive application using a MERN/Next.js stack and a fully serverless AWS architecture (Lambda, API Gateway, S3, CloudFront, Route 53), including CI/CD via the Serverless Framework. Implemented RBAC for student/faculty/admin users and added an article export feature backed by MongoDB.”
Poorna Vishnu
Mid-level AI Engineer specializing in LLM workflows and agent-based systems
“LLM/agent workflow engineer with production experience at T-Mobile, focused on scalable agent architecture and robust real-time evaluation/monitoring pipelines. Partnered closely with marketing and product to automate customer engagement and other business workflows, translating AI capabilities into measurable KPI impact via dashboards and continuous performance tracking.”
Niharika Bhasin
Intern Full-Stack Software Engineer specializing in AWS serverless and real-time web apps
“New-grad/early-career engineer who led high-stakes modernization of a field-operations platform from Firebase to AWS using an incremental/dual-write strategy, achieving zero downtime and ~30–32% infra cost reduction while improving scalability. Also built and productionized an AI-native code assistant (LangChain + Pinecone RAG) with measurable online metrics and safety guardrails, and has experience working directly with CEO/CTO/CPO and embedded with customer teams to ship enterprise features quickly.”
Nikitha Margadi
Mid-level Data Engineer specializing in cloud lakehouse, streaming, and MLOps
“Data engineer at AT&T focused on large-scale telecom (5G/IoT) data platforms, owning end-to-end pipelines from Kafka/Azure ingestion through Databricks/Delta Lake transformations to serving analytics and ML. Has operated at very high volumes (~50+ TB/day) and delivered measurable performance gains (25–30% faster processing) plus improved reliability via Airflow monitoring, robust data quality checks, and resilient external data collection patterns (rate limiting, retries, dynamic schemas).”
Karan Javali
Mid-Level Full-Stack Software Engineer specializing in FinTech and cloud-native web platforms
“Software engineer with experience at Goldman Sachs and Arizona State University’s Learning Engineering Institute, shipping production backend systems including a vendor equities invoice-generation service designed for extensibility across multiple vendors. Built Django REST + PostgreSQL backends with JWT auth and Pytest coverage, and delivered data-heavy, responsive Angular dashboards; also has exposure to AWS EC2 deployments and GitLab CI/CD automation.”
Nikhitha santhoshi Maroju
Mid-level Full-Stack Software Engineer specializing in Java/Spring microservices on AWS
“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.”
Eric James
Senior Game Developer specializing in Unity, gameplay systems, and blockchain features
“Unity/C# gameplay engineer who has owned and shipped modular progression/rewards/inventory systems, including a separated blockchain integration layer that drove higher engagement while keeping performance stable at scale. Also implemented AI behaviors with Unity ML-Agents using a training/inference separation approach, and has hands-on experience debugging platform-specific state issues during console certification across multiple shipped platforms (mobile/console/PC/WebGL).”