Vetted OpenCV Professionals

“ML/NLP practitioner focused on knowledge-graph-based retrieval for LLM question answering, including an urban/autonomous-vehicle decision-making use case. Built a hierarchical GraphRAG + vector database system and an entity-resolution pipeline that blends spatial and semantic similarity, validated using LLM-generated synthetic datasets; uses Python tooling like RDFLib, GraphDB, OpenAI APIs, and LangChain.”

“Worked at AMD on Vivado tool releases, focusing on adding and integrating new IP/SoC functionality into the Vivado flow and validating expected behavior through testing. Comfortable engaging with customers and open to travel for hands-on customer-facing work.”

“Built and scaled a multi-agent LLM automation pipeline during a fintech internship, growing from a rapid 1-week proof-of-concept to a 15+ agent hierarchical system that cut market brief report generation time from ~5 hours to under 30 minutes. Hands-on with agent frameworks (Haystack, CrewAI, LangChain) and experienced in debugging agent communication issues via sandboxed modular testing and context/token management; also regularly gives architecture-first technical demos at multiple hackathons and university events.”

“GenAI/ML engineer who has shipped production agentic systems in highly regulated and high-throughput environments, including an AWS Bedrock-based fraud/compliance workflow at U.S. Bank with PII redaction and hallucination detection that cut investigation time by 50%+. Also built and evaluated RAG and recommendation systems at Target, using RAGAS-driven testing, hybrid retrieval with re-ranking, and SHAP explainability dashboards to align model behavior with merchandising business KPIs.”

“Master’s robotics candidate at Northeastern (Silicon Synapse Lab) who built and tuned an NMPC for the M4 multi-modal morphobot to achieve high-speed (>10 m/s) aggressive flight maneuvers and even hover under a full rotor failure, using MATLAB/CasADi/Simulink/Simscape with IPOPT. Also has ROS/ROS 2 experience spanning SLAM/navigation on a UGV and GPS/IMU sensor-fusion + dead-reckoning with custom ROS 2 nodes/messages, with a strong simulation-first and real-time debugging approach.”

“Healthcare-focused LLM engineer who has built two production clinical applications: an automated structured clinical report generator from physician-patient conversations and a RAG-based chatbot for retrieving patient history (procedures, allergies, etc.). Demonstrates strong applied RAG expertise (overlapping chunking, entity dependency graphs, temporal filtering, graph RAG) to reduce hallucinations/omissions and partners closely with clinicians to automate hospital workflows.”

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

“Backend-focused Python/Flask engineer who owned REST APIs for a video analysis system, including preprocessing, ML inference integration, and post-processing into time-aligned predictions consumable by a React UI. Demonstrated practical performance/scalability work by decoupling API request handling from CPU-heavy processing and adding timing instrumentation to identify and optimize bottlenecks.”

“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”

“At HPE, led and deployed an enterprise-grade LLM document intelligence platform for an insurance client, automating extraction from highly variable PDFs/scans/emails and raising field accuracy from 74% to 93%. Built a LangChain/Pinecone/OpenSearch RAG framework to cut hallucinations by 37% and operationalized LangSmith evals in CI, driving a 41% triage accuracy lift and >33% fewer incorrect resolutions while partnering closely with claims operations via HITL workflows.”

“Built and deployed a production LLM-powered clinical insights/summarization assistant for healthcare teams, including a Spark+Airflow pipeline, fine-tuned transformer models, and a FastAPI Docker service on AWS. Demonstrates strong MLOps/LLMOps depth (Airflow on Kubernetes, custom AWS operators/IAM, MLflow, CloudWatch) and practical reliability work like hallucination mitigation, confidence scoring, and retrieval-backed evaluation with shadow deployments.”

“Currently a startup CTO who thrives on building new technology stacks and rapidly turning technical ideas into products. Interested in partnering with a CEO/business team to commercialize embedded/edge concepts such as multi-sensor drone localization (video/audio/RF with SDR), low-cost solar+battery power nodes networked via LoRa, and an Amazon Sidewalk/LoRa connectivity device with cloud management.”

“Built and shipped LLM/RAG systems in finance and startup settings, including a Goldman Sachs document intelligence platform that indexed ~8TB of regulatory filings and delivered cited, conversational answers with <2s latency—cutting compliance research by ~4.5 hours per batch. Also developed LangChain-based agent workflows at Finta to automate CRM enrichment and investor lookup with strong testing, tracing (LangSmith), privacy guardrails, and auditability.”

“Currently working in CoRAL Lab, built and deployed IntegrityShield—a document-layer PDF watermarking system that keeps assessments visually identical while disrupting LLM-based solving; validated in a real classroom where it helped catch 12 AI-cheating cases. Also built MALDOC, a modular red-teaming platform for document-processing AI agents using LangGraph to run reproducible, deterministic adversarial trials across OCR/text/vision routes.”

“ML/LLM engineer who has deployed a production LLM-powered assistant for intent classification and query routing (order recommendation/support deflection), combining BERT fine-tuning with an embedding-based retrieval layer and optimizing for low-latency inference. Experienced with end-to-end reliability practices—Airflow-orchestrated ETL, data validation/alerting, MLflow experiment tracking, and iterative improvements driven by user feedback and monitoring.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”

“Internship experience at Qualitest building and deploying an LLM-powered test automation system that reduced manual test creation and improved efficiency (~40%). Demonstrates strong production engineering for LLM systems (timeouts/retries/monitoring/caching, prompt optimization, batching) and has scaled workflows to 100+ concurrent jobs; also has orchestration experience with AWS Step Functions and Kubernetes.”

“Robotics software engineer with hands-on ROS2 + Autoware perception experience, focused on building benchmarking infrastructure for object detection models inside a real-time autonomous driving stack. Strong in evaluation rigor (synchronization, deterministic playback, format standardization) and practical ROS2 debugging/validation workflows using RViz and Gazebo.”

“Built and deployed a production agentic LLM knowledge assistant that answers complex questions over internal documents, APIs, and databases using a RAG architecture (FAISS/Pinecone) and LangChain/LangGraph orchestration. Emphasizes production-grade reliability and hallucination control through grounding, confidence thresholds, validation, retries/fallbacks, and full observability (logging/metrics/traces) with continuous evaluation and feedback loops.”

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

“AI engineer who built and shipped a production AI document-understanding/search system at Sumeru Inc, including a full RAG + LLMOps evaluation stack (MLflow, DeepEval, RAGAS) deployed on GCP. Also developed LangChain/LangGraph multi-agent workflows for UAV flight-log analysis and has experience presenting AI solutions to non-technical stakeholders and prospect clients to drive POCs.”

“Built a production-oriented multimodal RAG "Fix Assistant" with FastAPI, Tavily search, BM25 + cross-encoder reranking, and a local Phi-3.5 model, emphasizing strict grounding and fallback/verification modes to prevent hallucinations. Also has hands-on federated learning experience using STADLE to orchestrate edge-node training and aggregation for EV telemetry data, plus experience communicating AI results to non-technical stakeholders (traffic RL/congestion outcomes).”

“Robotics software engineer/researcher (master’s work) who built a human-aware motion planning stack for a UR16/UR16e arm: RGB-D 3D skeleton perception in ROS2, deep-learning-based human motion prediction, and MoveIt2-integrated real-time planning with a Gazebo digital twin. Demonstrated strong real-time optimization (profiling + GPU offload with CuPy/TensorRT) and practical systems skills spanning safety validation, visualization, and low-level comms (CAN/SocketCAN) on embedded deployments (Jetson, Docker, Autoware/Ouster).”

“New grad SDE (AI/ML) who built and deployed an LLM-based chatbot framework used across technology, military, and banking contexts, focusing on model selection tradeoffs (latency vs accuracy) through prototyping and benchmarking. Also built a multi-agent "eaterybot" using PyAutoGen/AutoGen with a manager agent orchestrating specialized agents, and emphasizes rigorous testing with adversarial/edge-case datasets and hallucination checks.”