Vetted Reinforcement Learning Professionals

“LLM/MLOps engineer who has shipped a production RAG-based technical documentation assistant (FastAPI) cutting manual review by 45%, with deep hands-on retrieval optimization in Pinecone/LangChain (HNSW, hybrid + multi-query search, caching). Also brings healthcare domain experience—building Airflow-orchestrated EHR pipelines and delivering FDA-auditability-friendly predictive maintenance solutions using SHAP/LIME explainability surfaced in Power BI.”

“Robotics software architect who built an automated pick-and-place palletizing prototype at BLACK-I-ROBOTICS, spanning perception (multi-RealSense fusion, segmentation, 6D pose, ICP), GPU-accelerated motion planning (MoveIt 2 + NVIDIA CuRobo), grasp generation, and safety (human detection + safe mode). Also brings cloud/CI/CD depth from VERIDIX AI (AWS Cognito/Lambda/ECS and CodePipeline stack) and demonstrated strong debugging chops by reducing outdoor rover EKF drift to ~5 cm via Allan variance-based IMU tuning.”

“LLM/GenAI engineer who built a production AI-powered credit risk policy summarization and compliance alerting platform at HCL Tech, focused on factual accuracy and auditability for a financial client. Implemented a multi-retriever LangChain RAG architecture with citations-only prompting, fallback agents, and human-in-the-loop legal review—cutting manual review time by 35% and scaling to 12 teams.”

Santhoshi Priya Sunchu

Screened

Mid-level Data Scientist specializing in NLP and predictive modeling

Massachusetts, USA5y exp

“AI/ML practitioner in healthcare/insurance (Blue Cross Blue Shield) who built and deployed a production NLP system to classify patient risk from unstructured clinical notes. Experienced in end-to-end pipeline orchestration (Airflow, AWS Step Functions/Lambda/SageMaker) and real-time optimization (BERT to DistilBERT on AWS GPUs), with strong clinician collaboration to drive adoption.”

Pravalika Kuppireddy

Screened

Mid-level AI/ML Engineer specializing in Generative AI and intelligent automation

4y exp

“LLM engineer who built and productionized a system to classify GitHub commits (performance vs non-performance) using zero-/few-shot approaches over commit messages and diffs, working at ~5M-record scale on multi-node NVIDIA GPUs. Experienced orchestrating end-to-end LLM pipelines with Airflow and GitHub Actions, and emphasizes reliability via testing, guardrails, and observability while collaborating closely with non-technical product stakeholders.”

“Built and shipped a widely adopted, production-grade RAG internal search assistant that unified scattered engineering knowledge, deployed as a FastAPI service on Kubernetes with FAISS + LangChain. Demonstrates deep practical expertise in retrieval tuning (chunking, hybrid search, re-ranking) and in making LLM workflows reliable in production via guardrails, monitoring, and evaluation, plus strong cross-functional delivery with non-technical operations teams.”

Anirudh Venkatesh

Screened

Mid-level XR/Unity Developer specializing in AR/VR and immersive applications

Riverside, CA5y exp

“Unity developer with hands-on experience across VR, AR, multiplayer, and AI-driven gameplay systems. They’ve owned end-to-end interaction architecture for VR cognitive testing focused on accessibility, integrated Gemini-based command systems into gameplay, and shipped cross-platform immersive applications spanning Magic Leap 2, iOS, and Photon-powered multiplayer experiences.”

“UE5 Blueprint-focused gameplay/system designer who shipped an XR interactive experience (“Echoes of the Moon”), owning interaction logic, progression triggers, state management, and animation syncing through ship. Emphasizes modular component-based architecture with interfaces and data-table-driven tuning, plus strong profiling/optimization skills (refactoring Tick-heavy systems to event-driven) validated through 20–30 user playtests and mobile XR (Android) frame-budget constraints.”

“Robotics software engineer who built a full LiDAR SLAM pipeline from scratch in C++ (ICP, pose graph optimization, loop closures) and validated it quantitatively against ground-truth datasets. Extensive ROS2 experience from academics and an internship building a localization system, plus practical deployment work using Docker across x64 and ARM edge devices; also trained RL policies for TurtleBots in Gazebo.”

“Robotics software candidate whose thesis focused on multi-robot warehouse coordination using MAPPO reinforcement learning, trained in simulation (LBF environment, Isaac Sim/RViz) and deployed onto three real-time robots. Built custom ROS 2 Humble nodes for multi-robot control with namespaces, TF broadcasting, and an RL pipeline integrating LiDAR odometry and camera observations.”

“Robotics software engineer with hands-on experience building Visual-Inertial SLAM and ROS2 sensor-fusion pipelines for autonomous warehouse forklifts (ArcBest), including rigorous calibration (AprilTags, Allan variance, temporal sync) and recovery features like pose injection. Also implemented RL-based local planning at RollNDrive using Isaac Sim with domain randomization to bridge sim-to-real, improving real-world navigation success back to ~90% after initial deployment.”

“Robotics software engineer with a Master’s in Robotics who built a digital twin of an excavator by creating a high-fidelity URDF (kinematics, joint limits, inertial properties) to stress-test controllers near saturation/limit conditions using ROS2 + MoveIt. Has hands-on ROS/ROS2 experience building perception (AprilTag/OpenCV) and sensor interface nodes (IMU/encoders/CAN), plus data-driven debugging and SLAM tuning for GPS-denied navigation using ROS bags and loop-closure validation.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

“Built an Arduino-based obstacle-avoiding robot using sonar/laser sensors and improved performance from 0.60 to 0.87 accuracy through sensor-fusion thresholding and iterative tuning. In an internship, optimized a legal-document NLP pipeline by switching to a distilled/quantized transformer and offloading inference to a GPU-backed Flask service, cutting inference time by 40%+ without added infrastructure spend.”

“Full-stack engineer in healthcare and enterprise analytics who has shipped event-driven, near-real-time systems (Spring Boot microservices + Kafka + AWS) and large-scale patient/provider portals (50k+ users). Strong in production reliability and performance—measurably reduced claims latency (27%), cut support tickets (25%), and handled real AWS scaling incidents end-to-end. Also built a Python REST control plane for SDN routing integrated with external reinforcement learning agents.”

Koushik Alapati

Screened

Mid-level Robotics Software Engineer specializing in perception, localization, and autonomous navigation

New York, USA4y exp

“Robotics software engineer with hands-on ROS2 experience building perception-driven navigation for AMRs, integrating YOLO11 + Depth Anything V2 and multi-sensor fusion (LiDAR/RGB-D/IMU) to boost pose accuracy by 30%. Strong in real-time debugging and edge deployment on NVIDIA Jetson (ONNX/CUDA), plus cloud-enabled telemetry (Azure) and simulation-driven testing (Isaac Sim) that cut physical test cycles by 25%.”

Ponugoti Sushma

Screened

Mid-level Machine Learning Engineer specializing in IoT, edge AI, and enterprise ML

Texas, USA5y exp

“Built and productionized an LLM/RAG question-answering service over technical documentation, focusing on retrieval quality (reranking + IR metrics), latency, and scaling. Experienced orchestrating end-to-end ETL/ML workflows with Airflow/Prefect/AWS Step Functions and improving reliability via parallelism, retries, and shadow testing. Also delivered an explainable healthcare risk-flagging classifier with a stakeholder-friendly dashboard for a non-technical program manager.”

Sreedivya Nagalli

Screened

Junior AI/ML Engineer specializing in deep learning and full-stack ML applications

2y exp

“Built and operated a production-used RAG-based AI study planner (GPT-4 + FAISS) that handled 250+ concurrent users, with real-world reliability engineering (caching, fallbacks, schema validation, Redis state, monitoring). Also has healthcare data integration experience at Medinet Analytics, standardizing messy EHR/practice-management data with canonical schemas, idempotency hashing, and compliance-grade audit trails.”

Lakshmi Priya Ramisetty

Screened

Mid-level ML & Data Engineer specializing in GenAI, graph modeling, and fraud/risk analytics

Redwood City, CA5y exp

“Built a production AI fraud/risk scoring platform at BlueArc that ingests web business/product/site data, generates text+image embeddings, and connects entities in a graph to detect reuse patterns and links to known bad actors. Optimized for scale with incremental graph re-scoring and delivered investigator-friendly explainability by surfacing the exact signals/relationships behind each score; orchestrated workflows with Airflow and GCP event-driven components (Pub/Sub, Dataflow, Cloud Run) and has recent LLM workflow orchestration experience (retrieval, prompting, scoring).”

Sravya Chunduri

Screened

Mid-level AI/ML Engineer specializing in LLM, NLP, and MLOps

Virginia, USA4y exp

“AI/ML Engineer with 3+ years of experience spanning RAG pipelines, MLOps, large-scale data workflow automation, and resilient Playwright-based UI automation. At Black Hawk Network and Wipro, they describe shipping production systems with strong observability and compliance controls, including reducing flaky automation failures from 30% to under 2% and automating 3+ TB/day reconciliation workflows.”

“Robotics software engineer with a Master’s thesis building an end-to-end monocular-vision pick-and-place controller for construction use cases on TurtleBot3 + OpenManipulator, spanning synthetic data creation, transfer learning, simulation in Gazebo, and real-robot deployment. Leveraged ROS distributed architecture to run two heavy AI models across networked GPUs to achieve usable real-time performance, and has production CI/CD experience as a Senior Software Engineer in AI/analytics.”

“Robotics/perception engineer who led and owned ARC, a cooperative perception system for autonomous vehicles that aligns and fuses multi-vehicle LiDAR point clouds in real time. Built a ROS-based multi-node pipeline with grid-based spatial reasoning and motion-compensated data sharing, achieving <20 ms compute latency and sub-7 cm alignment error; accepted to ACM SenSys 2026.”

“AI/ML engineer with experience at Northern Trust and Persistent Systems building production LLM + RAG systems for regulated financial use cases, including liquidity forecasting, anomaly detection, and credit scoring. Emphasizes compliance-first design with explainability (SHAP), traceability (MLflow), and hallucination controls (FAISS + citation-grounded prompting), and has delivered drift-triggered retraining pipelines using Airflow and Kubernetes while translating model outputs into business-ready marketing segments.”

“Graduate researcher who builds production-grade AI systems spanning LLM security evaluation and on-device RAG. Created HoneyLearner, a self-learning attack framework using GPT-4-class models as structured black-box attackers against honeywords defenses, with rigorous metrics and reproducible orchestration (Airflow/Spark/Kafka/Docker). Also partnered with agriculture scientists at Texas A&M–Corpus Christi to deliver UAV + 3D point-cloud crop-stress maps that cut time-to-insight ~40% and enabled ~30% earlier interventions.”