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Vetted PyTorch Professionals
Pre-screened and vetted.
Subhasmita Maharana
Mid-level Data Scientist specializing in NLP/LLMs, time series forecasting, and MLOps
“Data/ML practitioner with hands-on experience building NLP systems from prototype to production: delivered a Twitter sentiment classifier with robust preprocessing, SVM modeling, and Power BI reporting, and built entity-resolution pipelines for messy multi-source customer data (reporting ~95% improvement in unique entity identification). Also implemented semantic linking/search using SBERT embeddings with FAISS vector retrieval and domain fine-tuning (reported ~15% precision lift), and applies production workflow best practices (Airflow/Prefect, Docker, Azure ML/Databricks, Great Expectations).”
Piyush Modi
Intern Software Engineer specializing in backend systems, cloud infrastructure, and ML/LLM tooling
“Infrastructure-leaning engineer who has built real-time ML systems end-to-end: a Jetson-deployed adaptive Whisper ASR service (Flask + WebSockets, React/TS UI) and a high-throughput Postgres schema for live transcription. Also delivered customer-facing AI billing/OCR improvements for a dental startup (Dentite), boosting OCR performance by 38%, and has experience instrumenting open-source ML deployment stacks to add infrastructure visibility.”
Samuel Audu
Staff Platform Engineer specializing in multi-cloud platforms and internal developer portals
“Infrastructure reliability/capacity-focused engineer with hands-on IBM Power/AIX (LPAR/DLPAR, HMC, VIOS) performance troubleshooting and modern cloud-native delivery experience. Built production CI/CD and Terraform-managed AWS/EKS environments, and has led real incident recoveries spanning Kubernetes autoscaling and AWS quota constraints with concrete RCA and prevention improvements.”
Sahithya Godishala
Mid-level AI/ML Engineer specializing in GenAI, LLMs, RAG, and MLOps
“Built and deployed a production LLM-powered RAG document intelligence/Q&A system for healthcare prior authorization, reducing manual medical document review time and improving decision efficiency. Strong in end-to-end LLM application engineering (LangChain/LangGraph), retrieval quality improvements (hybrid search, embedding tuning, chunking strategies), and rigorous evaluation/monitoring for reliability.”
Mahesh Kumar Duvvuri
Senior Full-Stack Software Engineer specializing in microservices and cloud-native systems
“Backend/infra engineer with experience across Nestle, J.P. Morgan, and Capgemini, combining ML systems work (YOLOv8/PyTorch object detection with TFLite edge deployment) with production-grade cloud/Kubernetes operations. Has delivered measurable impact via AWS migrations (25% cost reduction, 99.9% availability), microservice modernization (35% faster processing), and low-latency Kafka streaming for financial dashboards (<100ms) using DLQs and idempotent consumers.”
Abhinav Gupta
Junior Machine Learning Engineer specializing in LLMs and applied data science
“Built and shipped multiple production AI systems, including Auto DocGen (LLM-generated OpenAPI docs kept in sync via AST diffs, schema-constrained generation, and CI/CD on Render) and a multimodal sign-language recognition pipeline at USC orchestrated with FastAPI, MediaPipe, and PyTorch. Also partnered with Esri’s non-technical community team to fine-tune an LLaMA-based spam classifier with a review UI, cutting moderation time by 70%.”
Ajay Madhusudhan Thumala
Junior Software Engineer specializing in data engineering and LLM applications
“Computer science engineer and master’s graduate who independently built a mechatronics-heavy capstone prototype: a smartphone concept for deafblind users using micro-actuator arrays for braille reading. Also has platform engineering experience at Quantiphi, deploying webhooks to Kubernetes and implementing GitOps-based CI/CD using AWS CodeCommit/CodeBuild and ECR.”
Nikhil Soni
Junior AI/ML Engineer specializing in LLM systems and retrieval-augmented generation
“Built and deployed a production LLM-powered market intelligence and decision-support platform for noisy, real-time financial data, using a high-throughput embedding + vector DB RAG architecture to reduce hallucinations while keeping latency and cost low. Operated it at scale with GPU-backed inference (continuous batching/quantization), FastAPI on Kubernetes, and Airflow-orchestrated ingestion/embedding/retraining workflows, with strong schema-based reliability and monitoring.”
Junior Robotics & Controls Engineer specializing in UAV autonomy and embedded systems
“Robotics software engineer focused on autonomous drones and mobile robotics: implemented a sliding mode inner-loop controller and a RealSense T265 VIO state-estimation pipeline integrated into ArduPilot EKF3 for GPS-denied indoor flight. Strong simulation-to-deployment experience (Gazebo/MAVROS to firmware), ROS2 networking/debugging, and hands-on validation through multi-sensor trials and log analysis.”
Manasa Mangipudi
Mid-level Machine Learning Engineer specializing in NLP and computer vision
“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.”
Subbu Paravatareddy
Engineering Leader specializing in cloud modernization and AI/ML integration
“Player-coach engineering leader focused on buyer/distribution product lines, building scalable purchasing/planning frameworks and modernizing workflows. Drove performance and reliability improvements via queue-based async architectures, external API redundancy, and CI/CD automation, and has led production incident response (cache-related) with follow-up playbooks and monitoring. Experienced in high-growth/startup environments, combining hands-on delivery with mentoring, 1:1s, and performance coaching.”
Rui Cheng
Mid-level Software Engineer specializing in autonomous driving simulation and 3D mapping
“Founding software engineer who built an autonomous-vehicle 3D digital twin using Unreal Engine 5 and CARLA, owning core simulator logic (traffic/scenarios/weather) and a ROS 2-based pipeline to record synchronized multi-sensor data (RGB/depth/segmentation/LiDAR/IMU/GPS). Also implemented distributed synchronization patterns (server + client prediction) using FastAPI and WebSockets; seeking roles with H1B transfer and targeting ~$110k.”
Yui Chang
Mid-level Robotics Engineer specializing in autonomous navigation, SLAM, and MPC control
“Autonomous marine surface algorithms engineer at CURLY contributing across the full autonomy stack in ROS 2 (C++/Python), from GNSS-IMU InEKF localization (100 Hz) and GTSAM object-level SLAM to semantic mapping and A*/Lie-group MPC planning/control. Strong focus on real-time optimization for constrained embedded hardware, with disciplined debugging/validation using ros2_tracing, rosbag2 replay, and Gazebo, and reproducible deployment via Docker/CI.”
Sanjana Duvva
Mid-level AI/ML Engineer specializing in Generative AI, LLMOps, and MLOps
“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.”
Jen-Hung Chang
Mid-level Software Engineer specializing in cloud infrastructure and distributed systems
“Backend/platform engineer who built an AI RAG system on FastAPI/Postgres/AWS with 10+ microservices, vector search optimization (ANN + two-stage re-ranking), and GitOps-driven CI/CD that cut deploy time from hours to minutes. Also deployed Java identity services on Kubernetes at TSMC for 200K+ users using ArgoCD/Azure Pipelines, and built a reliable real-time IoT pipeline (MQTT/Node/MongoDB) with strong consistency controls.”
Yunxi Zhu
Entry-level Robotics & Automation Engineer specializing in robot learning and manufacturing automation
“Robotics software engineer focused on real-time teleoperation and high-quality robot-learning data pipelines, including synchronized multimodal sensing (RGB-D, tactile, joint states) for Diffusion Policy training on a bimanual ALOHA robot. Strong ROS practitioner who debugs real-time control issues with ROS tooling and builds simulation environments in Isaac Lab and PyBullet; also packages data-collection stacks with Docker.”
Karan Variyambat
Mid-level Machine Learning Engineer/Researcher specializing in computer vision and multimodal AI
“Developed a production wildfire smoke detection system where smoke is visually subtle and easily confused with fog/clouds; addressed this with a hybrid CNN+LSTM+ViT model and multimodal weather features to reduce false positives. Experienced running scalable, reproducible ML pipelines on shared GPU infrastructure using Slurm and Kubernetes-style batch jobs with checkpointing, retries, and rigorous error analysis.”
Rajesh Nagula
Mid-level Robotics Software Engineer specializing in real-time control and perception
“Robotics software engineer focused on controls and motion planning for autonomous flight systems using ROS 2 (rclcpp), Gazebo/RViz, and BehaviorTree.CPP. Has hands-on real-time control experience (1ms loop rate) and has improved system performance by tracing latency issues and refactoring vision components (singleton camera init). Also built low-latency Ethernet/TCP comms on top of the IgH Ethernet stack and uses digital-twin simulation (Gazebo, MuJoCo; beginner Isaac Sim) to validate algorithms.”
Sri Harshitha Yannam
Junior Software Engineer specializing in AI/ML and cloud platforms
“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.”
Ishaan Nanal
Intern-level Software Engineer specializing in backend systems and AI/ML
“Built and shipped an LLM-powered RAG research copilot used by 20+ users across biology, physics, and ML, cutting literature review from days to minutes. Strong focus on production reliability—iterated on chunking/retrieval/prompting, added validation and modular pipelines for debuggability, and is now containerizing and scaling the system with Docker and GCP.”
Harrishkumar Loganathan
Mid AI/Machine Learning Engineer specializing in FinTech and Generative AI
“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.”
Hsiang-Chen Chiu
Junior AI Software Engineer specializing in computer vision and generative AI
“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.”
Yunjie Liu
Junior Software Engineer specializing in bioinformatics and full-stack development
“Built and stabilized production data pipelines in clinical genomics, including integrating a qPCR step into Baylor Genetics' workflow with a focus on reliability, turnaround time, and reducing manual intervention. Also has hands-on LLM production experience, creating a Python/OpenAI-based translation evaluation pipeline that reduced manual review time by 70% and improved scoring consistency.”