Vetted Deep Learning Professionals

“LLM engineer who has deployed privacy-preserving, real-time workplace risk monitoring over massive enterprise chat/email streams, tackling latency, hallucinations, and extreme class imbalance with model benchmarking, RAG + fine-tuning, and a pre-filter alerting layer. Also built an agentic legal contract drafting system (Jurisagent) using LangGraph/LangChain with deterministic multi-agent control flow, structured outputs, and reliability-focused evaluation/telemetry.”

“Software engineer who owned customer-facing, high-traffic TypeScript/React + TypeScript backend systems end-to-end, emphasizing safe velocity through feature flags, staged rollouts, observability, and rollback-ready incremental delivery. Reports shipping more frequently with fewer production incidents and faster recovery due to these guardrails.”

“Built and shipped a production LLM-powered investor research feature for a fintech product, focused on grounded answers and minimizing hallucinations. Implemented retrieval-quality and evidence-coverage gating with clear refusal fallbacks, and evaluates systems with regression tests and metrics like correct-refusal rate, hallucination rate, and latency. Comfortable orchestrating workflows with LangChain or custom Python depending on production needs.”

“AI/LLM engineer at Capital One who deployed a production RAG-powered fraud analysis and document intelligence platform using LangChain, OpenAI, Pinecone, Kafka, and AWS. Focused on reliability in real-time investigations via hybrid retrieval, schema-validated outputs, and LLM verification loops, reporting review-time reduction from hours to minutes and ~99% fraud detection precision.”

“Built and productionized a RAG-based analytics Q&A assistant for a financial analytics team, enabling natural-language querying across 200+ datasets (SQL tables, PDFs, compliance docs, wikis) and cutting turnaround time by 60%. Deep experience delivering regulated, audit-ready LLM systems on Azure (Azure OpenAI + LangChain) with strict grounding/citations, hybrid retrieval, and AKS-based low-latency deployment, plus strong collaboration with compliance analysts and auditors via iterative Gradio demos.”

“Engineering leader who has led 100+ engineers at Sainsbury’s Tech and previously scaled an org from 6 to 60+ at AND Digital. Drove a high-impact modernization of a pricing/decisioning platform serving 1,700 stores—moving from batch monolith to real-time Kafka-based event-driven microservices with MLOps, IaC (Terraform), and zero-trust—delivering £18m+ annual profit uplift and 10+ deploys/day.”

“Built an "AskMyVideo" system that turns YouTube videos into queryable knowledge graphs by transcribing audio (Whisper), chunking and embedding content, and enabling traceable answers back to exact timestamps. Strong in entity resolution (rules + fuzzy matching + TF-IDF/cosine with PR-curve thresholding) and modern retrieval stacks (FAISS, hybrid dense/sparse, domain fine-tuning with ~12% precision gain), with a production mindset using Airflow/Prefect, Docker/FastAPI, and LangSmith/Prometheus/Grafana observability.”

Shanmukha Koganti

Screened

Mid-level AI/ML Engineer specializing in recommender systems and edge computer vision

Bay Area, CA6y exp

“ML/AI engineer with production experience at Shopify and Intel, building a deep learning product ranking system that lifted add-to-cart ~14% and serving real-time similarity search via FAISS+Redis under <20ms latency at massive scale. Also deployed computer vision models to 100+ retail edge locations using Docker/Ansible/k3s with zero-downtime rollouts, and applies strong MLOps practices (A/B testing, canary/shadow, observability) plus performance optimization (OpenVINO, INT8).”

Atulya Bist

Screened

Junior Data Scientist / Software Engineer specializing in LLM analytics and robotics

Los Angeles, CA3y exp

“Robotics/ML engineer who implemented TD3 and PPO in PyTorch to solve the challenging OpenAI Gymnasium humanoid-v5 MuJoCo task, including custom networks, rollout logic, and training scripts. Also has hands-on robotics coursework experience with ROS-based RRT motion planning on a real robotic arm, plus practical CI/CD and containerization experience (Docker, Jenkins, GitHub Actions). Currently exploring world models (VAE + sequence generator) using Euro Truck Simulator data.”

Sahil Kakadiya

Screened

Junior Embedded Systems & Wireless Software Engineer specializing in BLE/Wi-Fi performance

Mountain View, CA2y exp

“Master’s capstone contributor on an autonomous rover navigation project, serving as an embedded/robotics software designer. Built low-level wheel control and odometry from encoders, integrated RealSense and RPLidar via ROS, and solved sensor-fusion/coordinate-frame issues by creating custom TF transforms. Used Gazebo to debug sim-to-real behavior and improved reliability on rough terrain by moving to dual-channel encoders when IMU data proved unreliable.”

Prakash Bhanu

Screened

Director of Software Engineering specializing in cloud, platform, and FinTech systems

Sunnyvale, CA22y exp

“Senior software engineering leader with broad 0-to-1 product experience spanning web apps, microservices, monoliths, messaging platforms, ML/AI products, and large-scale distributed systems. Notable examples include building a payroll/finance product for cast and crew, a distributed messaging platform, and a Walmart application deployed across multiple CDNs and clouds handling hundreds of TPS, with personal ownership across architecture, design, coding, and support.”

Sanjay Santhanam

Screened

Mid-level AI Software Engineer specializing in LLMs and FinTech data systems

San Jose, CA4y exp

“Backend/AI systems engineer focused on productionizing agentic document-processing workflows for large financial PDFs. They describe owning deployments end-to-end, combining Python, Redis, LLM function calling, RAG/ReAct-style orchestration, and strong reliability practices to deliver 80% faster processing, reduce parsing errors from 12% to ~1%, and sustain 99.9% uptime in high-concurrency environments.”

“CMU candidate with hands-on experience taking LLM concepts from research prototypes toward production-ready designs (structured outputs, guardrails, failure-scenario evaluation). Also partnered with sales/customer teams at Mazecare to drive adoption with Dontia Alliance (largest dental clinic chain in Singapore) and engaged Singapore government stakeholders, bridging clinical workflow needs with IT security/integration concerns.”

“Robotics software engineer with Samsung Research America internship experience as primary developer on a real-time dense mapping system producing point clouds, plus a monocular depth-estimation framework using positional data. Hands-on ROS 2 and CAN integration from a University of Michigan autonomous shuttle project, and practical SLAM/motion-planning experience including handling the kidnapped robot problem and Dockerizing ORB-SLAM3 environments.”

“AI engineer with production experience building a high-accuracy (98%) fraud detection system operating at real-time latency (1–2s) over millions of transactions, using a multi-model pipeline approach to meet performance constraints. Also implemented Airflow-orchestrated workflows (DAGs, retries, alerts) to replace brittle cron scripts and is currently pursuing a master’s project on real-time ASL-to-text conversion.”

“Robotics software engineer with hands-on experience building a dual-arm (Kawasaki duAro) Cranfield assembly task-planning and motion-planning stack in ROS/MoveIt, using PDDL + behavior trees and OMPL for collision-free execution. Improved tight-tolerance insertions by integrating RGB-D visual servoing into the task planner loop, and also built an LLM-driven navigation pipeline with ORBSLAM3 for natural-language command parsing and real-time replanning.”

Venu Venkata Surendra reddy Erusu

Screened

Mid-Level Software Engineer specializing in AI/ML and Cloud-Native Microservices

Syracuse, NY4y exp

“Research assistant at Syracuse University who owned a Python/FastAPI analytics backend for user-uploaded large datasets, using S3 streaming uploads and background workers for heavy processing. Has hands-on experience deploying Dockerized Python/Java microservices to AWS EKS with Jenkins-based CI/CD, plus Kafka-based event-driven pipelines and practical migration patterns (dependency mapping, dual-write, reconciliation) to minimize downtime.”

Vamshikrishna Bandi

Screened

Senior AI/ML Engineer specializing in Generative AI and agentic multi-agent systems

6y exp

“Built and shipped a production LLM-powered multi-agent RAG system to automate complex internal support workflows, integrating tool execution (SQL/APIs) with validation guardrails to reduce hallucinations. Optimized for real-world latency and cost via model routing, caching, and async parallel tool calls, and enforced reliability with CI-gated golden test sets derived from anonymized production queries.”

Chancellor Day

Screened

Intern Robotics/Software Engineer specializing in autonomy, computer vision, and controls

San Diego, CA0y exp

“Robotics software engineer with a master’s focused in the field who has integrated a multi-sensor robotics fusion laser system (fault detection, PLC comms, PyTorch-based CV diagnostics, and an engineer-facing status front end) under NDA. Has ROS experience from the University of Michigan Autonomous Robotic Vehicle team using Nav2/SLAM Toolbox/Gmapping with RViz and ROS bag-driven debugging, plus Gazebo simulation work and upcoming drone path-planning optimization research.”

Vedant Kharwal

Screened

Intern AI/ML Engineer specializing in Generative AI and applied machine learning

Mumbai, India1y exp

“New graduate with hands-on LLM work building a RAG pipeline (HNSW, lexical reranking/boosting, ReAct) and optimizing it through ablation to dramatically reduce latency. Also building a modular personal assistant with a custom wake word model, router-driven agent selection, and integrations like Spotify with secrets managed via .env.”

Ishwari Mulay

Screened

Intern Data Scientist specializing in analytics and healthcare data

Philadelphia, PA1y exp

“Analytics candidate with AstraZeneca internship experience building scalable SQL and Python workflows on large healthcare datasets. Stands out for combining data engineering, reporting automation, and applied machine learning— including an end-to-end patient no-show prediction project that achieved 76.8% accuracy and reduced no-shows by 18%.”

“Built Baioniq, an enterprise LLM platform for automating extraction from massive unstructured documents like contracts and insurance claims. They demonstrate unusually strong production depth in agentic AI—scaling to 100k+ requests/day, processing 1M+ claim documents, and improving extraction accuracy through rigorous RAG architecture, evaluation, and fallback design.”