Vetted Research Assistants

“AI/LLM engineer who built a production RAG-based Text2SQL engine using Qdrant, including creating the underlying business/DB documentation, generating a test dataset, and designing detailed SQL-quality metrics for validation. Also partnered with non-technical stakeholders on a speech recognition project to prioritize medical terminology, improving accuracy through targeted corpora, lookup-table correction, and fine-tuning with a modified loss function.”

“Robotics/perception engineer focused on production-grade, real-time systems—optimized self-supervised segmentation on Jetson Nano from ~6–10 FPS to ~20–25 FPS and scaled experimentation/deployment by unifying 15+ edge models in a modular PyTorch Lightning framework. Experienced integrating distributed LiDAR-camera fusion via gRPC/protobuf into mission planning, migrating ROS1→ROS2 Foxy for multi-drone perception, and adding Prometheus-based observability for long-running deployments.”

Teja Babu Mandaloju

Screened

Mid-level Data Scientist/MLOps Engineer specializing in NLP, GenAI, and cloud ML platforms

Chicago, USA5y exp

“AI/ML engineer who led production deployment of a multimodal (text/video/image) RAG system on GCP using Gemini 2.5 + Vertex AI Vector Search, scaling to 10M+ documents with sub-second latency and +40% retrieval accuracy. Strong MLOps/orchestration background (Kubernetes, CI/CD, Airflow, MLflow) with proven impact on reliability (75% fewer incidents) and deployment speed (92% faster), plus experience delivering explainable ML (XGBoost + SHAP + Tableau) to non-technical retail stakeholders.”

Sai Bandaru

Screened

Mid-level Machine Learning Engineer specializing in fraud detection and LLM systems

Boston, MA6y exp

“At FiVerity, built and deployed a production LLM/RAG-based Information Gathering Tool for credit union fraud analysts that generates auditable investigation summaries from verified evidence. Focused on high-stakes constraints—hallucination prevention, cross-entity leakage controls, compliance/PII-safe monitoring, and latency—while also shipping customer-facing agentic workflows using CrewAI and LangGraph in close partnership with fraud and compliance stakeholders.”

“Built and shipped LLM-enabled decision systems focused on real production reliability rather than chatbot demos, including a multimodal radiology retrieval platform with 28% relevance gains and 35% lower latency. Also architected a 4-agent employee analytics workflow with structured outputs, traceable orchestration, and strong safeguards for messy real-world data.”

Clil Halevi

Screened

Junior Financial Markets Analyst specializing in quantitative research and FinTech

South Orange, NJ4y exp

“Analytics-focused candidate with internship experience at eToro and strong finance/product analytics exposure. They’ve worked on market sizing for Nordic stock launches, replicated a classic behavioral finance study using Python and CRSP data, and built cohort, retention, and churn analyses that informed onboarding and engagement recommendations.”

“Full-stack AI engineer who has owned production deployments for both a voice journaling/emotional insights app and a RAG-based research assistant. Stands out for turning messy, failure-prone LLM and document pipelines into reliable user-facing systems through strong debugging, staged workflow design, and post-launch stabilization.”

Kirill Aristarkhov

Screened

Junior Software Engineer specializing in full-stack, AI/ML systems, and game development

Santa Barbara, CA3y exp

“Full-stack engineer (React/TypeScript + Bun/Node-like backend) who recently rebuilt a terminal-based chat UI, implementing custom Markdown lex/parse/render and a typewriter-style streaming renderer while optimizing React DOM growth for ~50% faster performance. Has startup experience making high-ownership decisions under ambiguity and rapidly integrating multiple external AI/tooling services (5–6 in a week) with fallback strategies for flaky dependencies.”

Clea Ramos

Screened

Junior UX/UI Designer and Front-End Engineer specializing in accessible web products

Pennsylvania, USA4y exp

“UX/UI designer and frontend engineer with hands-on project leadership experience, including leading a 6-person engineering team to deliver an accessible video conferencing system for older adults in senior care. Particularly compelling for mission-driven or health-adjacent products: they combined stakeholder research, WCAG-minded design, frontend implementation, and post-launch testing to improve usability and cut system cost by 40%+.”

Ramya Sree Kanijam

Screened

Mid-level Software Engineer specializing in backend, cloud, and AI systems

Remote, USA3y exp

“Built and owned an end-to-end AI-driven content enrichment pipeline for a news workflow, using n8n, LLM agents, and external APIs to automate ingestion, deduplication, categorization, and approval routing. Stands out for production-minded AI systems work: they improved reliability with schema validation, retries, idempotency, and monitoring, while automating 90% of processing and cutting duplication errors by 95%+.”

“Robotics/ML candidate building an individual pedestrian trajectory forecasting system by adapting a GAN-style Social-GN training architecture from LSTM to a transformer-based AgentFormer design. Also has hands-on embedded robotics experience debugging lane-following behavior on a JetBot by tuning PID control, and uses Docker for reproducible training environments.”

“AI Engineer Co-Op at Northeastern University who built a production Patient Persona Chat Bot to help nursing students practice clinical interactions, fine-tuning Llama 3 and integrating a LangChain + Pinecone RAG pipeline deployed on Amazon Bedrock. Emphasizes clinical accuracy and reliability with guardrails, retrieval filtering, and continuous evaluation, and also brings strong data engineering/orchestration experience (Airflow, EMR/PySpark, ADF, dbt, Databricks, Snowflake).”

“LLM engineer who built a production-style educational questionnaire generation system (MCQs/fill-in-the-blanks/short answers) using Hugging Face models (BERT/T5) and implemented grounding, decoding tuning, and post-generation validation to control hallucinations and quality. Also developed a "tech care" assistant chatbot with a custom Python orchestration/router layer (intent classification, context management, multi-step flows) and a structured testing/evaluation approach including expert review and automated checks.”

“Full-stack engineer who built an oil & gas analytics dashboard backend using FastAPI, MongoDB, and Redis with a metadata-driven design for dynamic plotting. Shipped an LLM-powered chatbot (LangChain + tool/function calling) to let engineers query analytics in natural language, and also built a multi-step university chatbot workflow with Azure logging, confidence scoring, and human-in-the-loop review.”

Shivam Lahoti

Screened

Junior Full-Stack Software Engineer specializing in TypeScript/React and microservices

Boston, USA2y exp

“Software engineer who built and owned an internal workflow automation + analytics platform end-to-end (TypeScript/React/Node) with a microservices, RabbitMQ-based async architecture. Drove adoption by shipping iterative prototypes and prioritizing reliability/performance (Redis caching, query optimization), delivering ~30–35% latency improvements and ~30–40% reduction in manual operational work.”

Ramya Sree Kanijam

Screened

Mid-level Software Engineer specializing in LLM, RAG, and cloud AI

Corpus Christi, TX3y exp

“Recent master’s graduate who led a team project building an LLM-based chatbot with RBAC-controlled information disclosure and a focus on reducing hallucinations. Also has hands-on embedded robotics experience (Arduino obstacle-avoiding robot using ultrasonic sensors) and practical DevOps/cloud deployment exposure with Docker, Terraform, Jenkins, and AWS (EKS/ECS/CodePipeline).”

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.”

Jitesh Kumar S

Screened

Junior Machine Learning Engineer specializing in NLP, computer vision, and MLOps

Lafayette, IN3y exp

“ML/LLM engineer with Meta experience building production AI systems for near real-time user-report classification and summarization under strict latency (<250ms), safety, cost, and privacy constraints. Has hands-on MLOps/orchestration experience (Airflow, Spark, MLflow, Kubernetes, Docker, GitHub Actions) plus observability (Prometheus/Grafana) and applies rigorous evaluation, staged rollouts, and A/B testing to keep agent workflows reliable in production.”

“Built a production LLM-assisted workflow for customer configuration data migrations, combining agentic parsing with deterministic validation and fail-safe pipeline design. Stands out for turning messy ERP and operational data into reliable, repeatable transformations while improving accuracy and cutting manual effort by more than 80%.”

“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.”

“CoreAI intern at The Home Depot who improved the Magic Apron Assistant by building a production video ingestion + RAG retrieval system for long videos (uploads and YouTube), including a graph-based retrieval module to speed up and improve relevance. Experienced with Kubernetes orchestration (HPA) and production reliability practices like caching, monitoring, regression testing, and stakeholder-driven requirements.”

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%.”

Akhil Bharadwaj Mateti

Screened

Mid-level Software Engineer specializing in Data Science and Machine Learning

Arlington, Virginia4y exp

“Robotics/AV perception engineer who built a semantic-segmentation road detection system and integrated it into a ROS-based real-time pipeline (ROS bag camera feed to live monitor) achieving ~12 FPS. Strong in practical deployment work: solved multi-library versioning issues (ROS/OpenCV/TensorFlow), containerized the stack with Docker, and optimized inference by shifting runtime to C++ for large latency gains on NVIDIA hardware.”