Vetted Machine Learning Professionals

“Robotics software/vision engineer with hands-on experience building motion-tracking systems that fuse camera-based 3D tracking with IMU orientation to reproduce tool motion for automated spray painting. Has implemented ROS nodes/packages for Orbbec camera streaming and SAM3-based segmentation, plus CAN bus coordination between robots and Dockerized deployment for a pick-and-place robotic cell.”

“Paid media performance marketer with agency experience owning a high-spend account for ArcPoint Labs, running multi-channel Google Ads (search/display) and Meta campaigns to drive medical testing appointments and sales. Reported lifting ROAS from ~2x to ~6–7x within ~18 months while supporting expansion from 30 to 60 U.S. locations, using disciplined attribution, constant A/B testing, and structured campaign health audits.”

“Robotics software engineer (2 years) at a startup building a universal fleet management system, owning core integrations and real-time data pipelines for heterogeneous AMR/AGV fleets. Implemented Kalman-filter-based collision prediction integrating RTLS for human-driven forklifts, built MQTT microservices aligned with VDA5050, and is now architecting a PostGIS-backed path-planning service for dynamic, traffic-aware routing with future ML optimization.”

“AI Backend Engineer in MetLife’s claims technology group who built and deployed a production LLM-based decision support system that helps claim adjusters quickly find relevant policy rules from long PDFs and historical notes. Designed it as multiple production-grade services with retrieval-first guardrails, continuous validation, and Airflow-orchestrated pipelines for ingestion, embeddings, and vector index updates to keep the system reliable as policies and data evolve.”

“Backend/platform engineer with production experience building a Python SDK over a microservices ecosystem, emphasizing reliability (JWT auth, retries/timeouts, custom exceptions) and integration testing. Has delivered AWS EKS microservices with Jenkins+Helm CI/CD, strong secrets/config separation using AWS Secrets Manager, and set up Datadog APM/deployment/change monitoring. Also modernized legacy VB applications to C#/.NET WPF via incremental migration with parity testing and stakeholder sign-off.”

“Built and deployed an LLM/applied-ML system enabling efficient extraction of useful information from large unstructured multimodal datasets, owning the full pipeline from ingestion to inference and APIs with a strong emphasis on production reliability, latency, and monitoring. Also delivered a voice-based AI workflow for Hindi policy document access for the Election Commission of India by translating non-technical usability needs into iterative demos and a successful implementation.”

“Engineer with startup experience who emphasizes disciplined Agile execution (requirements analysis, Jira tasking, sprint planning) and production readiness (testing/QA/PR review). Uses profiling/logging for high-observability debugging and prioritizes incidents by impact. Has demoed engineering processes and worked directly with a client (Canadian music service) to position product capabilities and future extensions to drive adoption.”

“Robotics software engineer with multi-robot perception and ROS integration experience, including work on CoLoc-Net improving global visual descriptors (DINOv2-SALAD style) and training a metric head for scale-aware 3D pose/odometry with a UKF backend. Built a ROS node/GUI to synchronize monocular vision and radar outputs at ITRI, and independently created a custom camera driver to enable reliable image sharing across AgileX Limo robots under real hardware constraints.”

“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).”

“Undergrad who built a production RAG chatbot for a messy college website using OpenAI embeddings + FAISS, overcoming hard-to-crawl/non-selectable site content and strict API budget limits. Applies information-retrieval best practices (section-based chunking with overlap, precision/recall evaluation) and reliability techniques (edge-case testing, similarity thresholds, fallback responses), and has experience scaling similar indexing work to ~300,000 Wikipedia pages.”

“Built a production RAG-based healthcare chatbot to retrieve patient medical documents spread across multiple platforms, reducing manual and error-prone searching. Implemented semantic search with custom embeddings (Hugging Face) and Pinecone, deployed via FastAPI/Docker on AWS SageMaker with MLflow tracking, and optimized fine-tuning cost using LoRA while orchestrating retraining pipelines in Airflow.”

“Forward-deployed engineer who built a production AI drone-control chatbot that lets users fly a drone via natural language while viewing a real-time feed. Implemented RAG over drone SDK documentation (vector DB + top-k retrieval) and LoRA fine-tuning, with a focus on latency, token efficiency, and cost reduction, and regularly works with non-technical clients to integrate and explain AI system architecture.”

“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 and shipped a production LangGraph-based multi-agent LLM analytics/decision copilot that answers questions across SQL/BI systems and unstructured docs, emphasizing grounded, tool-verified outputs with citations and confidence gating. Deep hands-on experience with orchestration (LangGraph, CrewAI, OpenAI Assistants, MCP) plus real-world latency/cost optimization (vLLM batching/KV caching, speculative decoding, quantization) and rigorous eval/observability. Partnered closely with business/ops stakeholders to deliver explainable reporting automation, cutting manual reporting time by 50%+.”

“Frontend engineer who has led customer-facing web products end-to-end, with strong emphasis on scalable component architecture, design systems, and automated quality gates (CI + unit/integration/E2E). Experienced building complex React+TypeScript dashboards with thoughtful state separation and shipping fast via feature flags/canary releases while monitoring and optimizing real-world performance issues.”

“Built and owned end-to-end TypeScript/React dashboards with a Node.js backend, including post-launch additions like role-based access and new reporting views enabled by modular architecture and clean API boundaries. Also created an internal real-time operations/engineering dashboard that replaced spreadsheets and reduced manual tracking, iterating quickly based on direct team feedback.”

“Built and owned end-to-end ordering and inventory/order management systems for a wholesale distributor, delivering an MVP quickly and iterating based on direct observation of daily users. Experienced with TypeScript/React + Node.js layered architectures and microservices using RabbitMQ, including real-world scaling issues (duplicates, backpressure) and observability practices (correlation IDs, structured logging).”

“Built and deployed a production RAG-based AI assistant for sales reps to unify access to product info, pricing, and internal documents across multiple systems. Implemented ETL pipelines for normalization/chunking/embeddings, integrated the assistant into internal React/TypeScript UIs with user-specific context, and enforced security with private vector storage and permission-filtered retrieval.”

“ML/LLM engineer with production experience building a compliant RAG-based virtual assistant at Intuit, optimizing embeddings and FAISS retrieval (including PCA) for low-latency, privacy-controlled search and deploying via AWS SageMaker containers. Also built scalable Airflow+MLflow pipelines using Docker and KubernetesExecutor, cutting training cycles by 37%, and partnered with civil engineers/project managers at Aegis Infra to deliver predictive maintenance for construction equipment.”

“Backend/data engineer who builds Python (FastAPI) data-processing API services for internal analytics/reporting, emphasizing modular architecture, async performance tuning, and reliability patterns (health checks, retries, observability). Also migrated legacy on-prem ETL pipelines to Azure using ADF/Data Lake/Functions and implemented a near-real-time ingestion flow with Event Hubs plus watermarking to handle late events and deduplication.”

“Robotics software engineer who led the full perception/manipulation/planning stack for an autonomous watermelon-harvesting robot, including ripe-vs-unripe instance segmentation deployed on Jetson AGX Orin with TensorRT and quantization. Deep ROS 2 experience (custom ZEDx mask driver, LiDAR+stereo fusion, MoveIt 2/Nav2/ros2_control) and proven real-time optimization—cut latency ~40% and achieved consistent 7-second pick cycles in outdoor field conditions.”

“Backend/infrastructure engineer with experience across API development (FastAPI/MySQL/SQLAlchemy), Kubernetes deployments, and large-scale data processing—built a Dockerized Python pipeline to pre-aggregate ~1B Graylog events for efficient querying. Has enterprise infrastructure automation background at Hewlett Packard Enterprise (Datafabric) using Terraform/Ansible with fail-fast and rollback practices, plus Kafka-based sensor streaming prototypes to Google Cloud with Java workers and autoscaling.”

“Backend engineer who has owned end-to-end delivery of Python/FastAPI microservices for real-time data processing and alerting, including performance tuning (Postgres optimization, caching, async processing). Strong DevOps/GitOps background: Docker + Kubernetes deployments with GitHub Actions CI/CD and ArgoCD-driven GitOps, plus experience supporting phased on-prem to AWS migrations and building Kafka-based streaming pipelines.”

“LLM engineer/data engineer who has deployed production RAG systems for internal-document Q&A, building end-to-end ingestion, embedding, vector search, and FastAPI serving while actively reducing hallucinations and latency through rigorous retrieval tuning and caching. Also experienced in orchestrating cloud data pipelines (Airflow, AWS Glue, Azure Data Factory) and partnering with non-technical business teams to deliver AI solutions like automated document review.”