Vetted CI/CD Professionals

“Commercial UNIX specialist and former tech lead for a UNIX-based telco appliance spanning 11 SPARC Solaris servers, with strong Solaris troubleshooting (truss/iostat/netstat/snoop) and extensive shell scripting automation for safer, more consistent operations. Has executed multiple Solaris major-version migrations (6→8→10→11) and brings broad cross-UNIX platform experience (Solaris/SunOS/HP-UX/IRIX/OSF/1), while actively looking to deepen hands-on AIX/IBM Power expertise.”

“Co-founded a Digital Loss Prevention (DLP) startup and raised $6M in seed funding by showcasing a controlled, laptop-based technology demo. Post-funding, drove MVP planning and execution by sequencing operations and assembling a team to build an appliance MVP, using an iterative build/evaluate/visualize approach.”

“University robotics researcher graduating May 2026 who integrated an Intel RealSense D435i onto a TurtleBot3 (Jetson Nano) and built a ROS 2 node + OpenCV pipeline to feed color-based cues into navigation/path planning for RL grid-world experiments. Has hands-on ROS 2 experience spanning Gazebo simulation, Nav2, ros2_control, multi-robot namespacing, and ROS1-to-ROS2 bridging, plus CI/CD exposure (GitLab CI, Jenkins) from internships including aircraft navigation work.”

“Unity developer with hands-on VR experience focused on “game feel,” enhancing immersion by adding touch-reactive visuals, custom haptics, and spatialized binaural interaction audio in collaboration with an audio team. Has recent multiplayer experience using Unity Netcode and uses AI tools for code review and rapid upskilling (e.g., reactive programming). Seeking a fully remote role with compensation around €60–70k.”

“Robotics software lead with 4+ years of ROS/ROS2 experience spanning a startup (Inductive Robotics) and General Motors, building autonomous mobile manipulation and AMR material-handling stacks. Has hands-on depth in SLAM/navigation (Cartographer/Nav2), perception, and simulation, and has directly modified Cartographer to handle real-world sensor dropouts. Currently working on fleet-scale mapping capabilities (map merging/editing, trajectory pruning) for multi-robot deployments.”

“ML/GenAI practitioner with healthcare domain depth who built and deployed a production cervical-cancer EMR classification system using a hybrid rules + medical BERT approach, optimized for high recall under severe class imbalance and PHI constraints. Experienced running end-to-end production ML/LLM pipelines with Apache Airflow (validation, promotion/rollback, monitoring, retraining) and partnering closely with clinicians to calibrate thresholds and implement human-in-the-loop review.”

“Amazon Fire TV Devices engineer who built and shipped a production LLM-powered lab triage and validation system that grounds recommendations in internal runbooks/known-issue data and pushes evidence-based actions via dashboards and Slack. Emphasizes safety and measurability with structured JSON outputs, replay-based evaluation on historical incidents, and production metrics (e.g., disagreement rate and time-to-first-action), plus cost/latency optimizations like caching, batching, and rule-based fast paths.”

“LLM/agent engineer who has shipped production RAG chatbots in sustainability-focused domains, including a packaging recommendation assistant that standardized messy user inputs and used Pinecone-backed retrieval over product/regulatory data. Experienced orchestrating end-to-end ML workflows with Airflow and AWS Step Functions/Lambda, emphasizing reliability (property-based testing, circuit breakers, OpenTelemetry) and measurable performance (latency/cost). Partnered closely with non-technical leadership to ship 3 weeks early, driving adoption by 150+ businesses and ~20% reported waste reduction.”

“LLM engineer who has shipped production AI systems, including an RFP requirements extraction platform (OpenAI o4-mini + Azure AI Search + FastAPI) achieving 90%+ accuracy and ~5x throughput through grounding, structured outputs, parallelization, and caching. Also partnered with legal/compliance stakeholders at Nexteer Automotive to deliver an AI document comparison tool with traceability and confidence indicators, adopted by non-technical users and saving ~2 FTEs of review time.”

“Built and shipped a production-grade GenAI Fraud & Compliance Investigation Copilot for a large US bank, integrating OCR docs, structured data, and prior case history to generate grounded, regulator-friendly summaries and red-flag highlights. Demonstrates strong end-to-end LLM systems engineering (LangGraph/LangChain, hybrid retrieval with FAISS+BM25, guardrails/citations, streaming/latency optimization) plus rigorous evaluation and close partnership with compliance stakeholders.”

“Robotics software engineer focused on production-grade autonomy in GPS-denied environments, building full navigation stacks (perception, EKF/UKF sensor fusion, planning, control) in ROS2. Integrated YOLOv8/semantic segmentation/RL policies into real-time NAV2 pipelines via a custom perception-aware costmap layer, with emphasis on deterministic control loops, embedded GPU performance, and robust system observability/fault tolerance.”

“Engineering leader who drove TiVo IPTV’s client-facing API modernization from a monolith to AWS-based microservices (API Gateway, Lambda, EKS, Kafka, DynamoDB/RDS), including phased/blue-green production routing of millions of calls. Emphasizes org scaling through skill-based hiring, mentorship, and a you-build-you-run ownership culture, while balancing technical leadership with executive stakeholder communication and budgeting.”

“Technical Program Manager and one of three internal "founders" of HP’s Hardware-as-a-Service home printer subscription initiative, spanning DTC shipping, simplified setup, ink replenishment, and warranty coverage. Led roadmap definition/execution, scaled delivery rapidly to 15+ scrum teams (including vendors) with SAFe-like coordination and shared engineering standards, and drove key architecture trade-offs (building an interim subscription management service) to accelerate time-to-market by about a year.”

“Backend engineer who built a Python-based data profiling/statistics platform processing up to 50M rows and ~300 metrics, using a DAG execution model, multithreading, and smart caching to cut processing time by up to 70%. Also improved PostgreSQL query performance from 12s to 2s via indexing/query rewrites, integrated an LLM (LangChain + OpenAI) for explainable “chat with the pipeline” functionality, and designed an AWS EC2+SQS architecture for scalable, isolated per-user processing.”

“Backend-focused engineer who built an internal wiki LLM chatbot end-to-end using FastAPI, Kubernetes, and ChromaDB vector search, including frontend integration. Also has strong DevOps/migration experience—automating large work-item and repo migrations (Jira/FogBugz/ADO on-prem to cloud) via Python scripts, JSON mappings, REST APIs, and validation test suites.”

“Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).”

“Robotics software/perception engineer with production AMR experience at Symbotic, building a real-time SKU case re-identification pipeline used in high-volume Walmart/Target warehouse operations. Strong in ROS2 + Docker deployments on Jetson (TensorRT quantization) and system-level performance debugging, including cutting inference latency from ~13s to ~2s through architecture changes. Also has lab experience integrating SLAM/MPPI/behavior trees for rule-compliant navigation and distributed perception-to-UR5e manipulation systems (MoveIt/ros_control) with multi-camera sensing and 3D reconstruction.”

“Full-stack engineer who built a smart loan approval workflow for a Goldman Sachs hackathon (React/Node/Express/Postgres) including KYC handling, reviewer queues, and an ML-based pre-scoring/auto-reject step. Also has Amazon internship experience driving a customer-facing long-polling change that reduced empty requests by 84%, and demonstrates strong system design depth in real-time voice + LLM streaming architectures.”

“Backend engineer with SAP experience modernizing a legacy Flask/PostgreSQL product master data platform into a modular, stateless, containerized service with Kafka-based background processing and improved observability. Also has hands-on academic/side-project experience operationalizing ML (NLP retrieval with TF-IDF/BERT via FastAPI and CV lane-edge detection inference APIs using PyTorch).”

“Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).”

“Lead Data Scientist (10+ years) with recent work in healthcare data: built production pipelines that unify EHR, genomics, and clinical notes using NLP (spaCy/BERT/BioBERT) and scalable Spark-based processing. Also led development of domain-specific LLM/NLP systems for chatbots and semantic search, deploying models via FastAPI/Flask and improving retrieval with FAISS-backed, fine-tuned clinical embeddings and RAG-style workflows.”

“GenAI/LLM engineer and architect who built and deployed a production generative AI financial forecasting and scenario analysis platform at McKinsey, leveraging Claude (Anthropic), LangChain, Airflow, MLflow, and AWS SageMaker. Demonstrates strong LLMOps/MLOps rigor (monitoring, drift detection, automated retraining) and deep experience implementing global privacy controls (GDPR, differential privacy, audit trails) while partnering closely with finance executives and legal/IT stakeholders.”

“ML/NLP engineer focused on production GenAI and data linking systems: built a large-scale RAG pipeline over millions of support docs using LangChain/Pinecone and added a LangGraph-based validation layer to cut hallucinations ~40%. Also built scalable PySpark entity resolution (95%+ accuracy) and fine-tuned Sentence-BERT embeddings with contrastive learning for ~30% relevance lift, with strong CI/CD and observability practices (OpenTelemetry, Prometheus/Grafana).”