Vetted Bash Professionals

“Built and deployed a production AI “term explainer” agent that adapts explanations to beginner/intermediate/expert users by combining multi-step LLM reasoning with grounded Wikipedia retrieval. Owns end-to-end agent orchestration (smolagents/Python), reliability patterns (fallback across LLM providers, retries, guardrails), and observability/metrics-driven evaluation; also partnered with a non-technical researcher to deliver a plain-language research assistant agent.”

“LLM/MLOps engineer who has shipped a production RAG-based technical documentation assistant (FastAPI) cutting manual review by 45%, with deep hands-on retrieval optimization in Pinecone/LangChain (HNSW, hybrid + multi-query search, caching). Also brings healthcare domain experience—building Airflow-orchestrated EHR pipelines and delivering FDA-auditability-friendly predictive maintenance solutions using SHAP/LIME explainability surfaced in Power BI.”

“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.”

“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.”

“IBM Power/AIX infrastructure engineer with hands-on production experience across Power8/Power9 frames, VIOS and HMC, including resolving a production LPAR outage caused by vFC mapping issues. Has operated PowerHA clusters for critical finance workloads, running quarterly failover tests and handling an unplanned failover triggered by a network adapter failure, then improving resilience with redundancy and monitoring automation.”

“IBM Power/AIX infrastructure engineer who has owned a sizable production estate (50 Power servers / ~200 LPARs) spanning VIOS/HMC, SAN/NFS, and PowerHA clusters. Demonstrates strong incident leadership (LPAR outage + split-brain recovery) and a process-improvement mindset with measurable reductions in recurrence/MTTR, while also bringing modern DevOps/IaC experience (Jenkins, ArgoCD, Terraform, security scanning, canary/blue-green).”

“Built and launched a verified listings system for Burrow (student subleasing) after interviewing ~50 students about scam/fake listing concerns; chose a lightweight .edu-based verification approach to ship fast and then iterated with badges and clearer details, reducing churn from 15% to 7%. Also ran an LLM A/B test for auto-generating listing descriptions and improved trust/accuracy by updating prompts to prevent hallucinated details.”

“Founding/early engineer experience across Asante and a Series A startup (Adgency), shifting from data science/ML into owning production full-stack systems end-to-end. Built core product flows (registration, business profiles, map service), AWS-deployed gRPC microservices with CI/CD, and operated low-latency agent/video ad generation workflows with retries/fallbacks and PostHog-based observability.”

“Backend/AI engineer with production experience in payments/reporting systems and high-scale Node/NestJS services on AWS (ECS/ALB) using PostgreSQL, Redis, Kafka, Prisma, and Datadog. Shipped applied AI features including a Zendesk-embedded support copilot (summarization, draft replies, internal doc retrieval, playbook next steps) and an LLM-driven ops workflow agent with robust error taxonomy, retries/escalation rules, and auditability.”

“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.”

“ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.”

“JavaScript/React engineer with hands-on open-source library contribution experience, including thoughtful PRs that improved error handling, API flexibility, and added features backed by tests and documentation. Demonstrates a profiling-first approach to UI/runtime performance (memoization, component splitting, render-path optimization) and strong community support skills—reproducing edge cases, delivering sustainable fixes, and communicating workarounds and releases.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”

“Full-stack developer with strong cloud/on-prem focus (AWS, VPC networking) who has improved production reliability by bringing manually created IAM/security group resources under Terraform and standardizing environments. Demonstrated end-to-end troubleshooting across app + infrastructure + networking (traffic capture revealed proxy response truncation) and delivered Python-based monitoring/reporting enhancements that improved ops visibility and turnaround.”

“Built and shipped a production agentic LLM analytics platform that lets non-SQL business users query relational databases in plain English via a RAG + LangChain/LangGraph workflow and FastAPI service. Emphasizes safety and reliability with guardrails (validation/access control), testing/evaluation frameworks, and performance optimization (caching, monitoring, Dockerized scalable deployment), reducing dependency on data teams and speeding analytics turnaround.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

“Product-focused full-stack engineer (70% application-layer) who has shipped multi-tenant RBAC for a formerly single-tenant platform, cutting infrastructure costs by 50%. Built high-impact customer-facing features including analytics dashboards (40% retention lift) and a React/TypeScript scheduling grid that reduced navigation time by 60% and setup time by 80%, with solid AWS operations and Postgres performance tuning experience.”

“Full-stack engineer with strong ownership in fast-moving environments: designed and shipped a pre-order/campaign inventory system (NestJS + Strapi + Datadog) that freed 34% warehouse space and reduced stock risk to ~5.7%. Also built rapid, high-impact logistics features (Spot Sales) that drove last-mile cost to ~0 in ~40 days, and has hands-on AWS/Terraform/CI-CD experience including deploying a global RAG system with Pinecone, Datadog, and PagerDuty.”

“Software engineer who has owned and improved a customer-facing quote flow in a Vue/Nuxt app, using production observability to reduce latency and improve reliability via caching and request-handling fixes. Also shipped an internal LLM Q&A tool using embeddings + RAG over approved company docs and past support tickets, with guardrails, logging, and an evaluation loop that drove retrieval/prompt improvements. Seeking ~$110k base and requires H1B transfer sponsorship.”

“Full-stack engineer in healthcare and enterprise analytics who has shipped event-driven, near-real-time systems (Spring Boot microservices + Kafka + AWS) and large-scale patient/provider portals (50k+ users). Strong in production reliability and performance—measurably reduced claims latency (27%), cut support tickets (25%), and handled real AWS scaling incidents end-to-end. Also built a Python REST control plane for SDN routing integrated with external reinforcement learning agents.”

“Built and shipped a production RAG assistant using GPT-4, LangChain, and Pinecone/FAISS to search 50K+ institutional documents, with a strong focus on groundedness and hallucination reduction through retrieval optimization and re-ranking. Pairs this with a metrics-driven evaluation/monitoring approach (BLEU/ROUGE, manual sampling, logging) and workflow automation via Airflow, and has experience translating stakeholder needs into iterative AI prototypes.”

“QA automation engineer from Bajaj Finance who owned end-to-end automated test suites for large-scale web/mobile products (70M+ users), building Python and API automation integrated with Jenkins/Azure DevOps. Drove measurable quality outcomes (40% less regression effort, 35% fewer production defects, 98% successful UAT across 25+ releases) and has strong fintech lending domain experience (loan disbursement/repayment/eligibility).”

“Built and monetized an object-identification app end-to-end (FastAPI backend, HTML/JS frontend, SQLite→Postgres, auth, and an iOS wrapper via Capacitor/Xcode with Apple privacy/policy compliance). Also productionized an AI-native geospatial metadata/QA assistant using LLM+RAG plus deterministic Python validation, measuring impact via time-to-first-pass review and rework rate, and has experience modernizing legacy GIS workflows and delivering across USDA/FEMA-style teams with disciplined Jira-based execution.”

“Robotics engineer who built a smart campus tour robot on a Kobuki Turtlebot using ROS 1, implementing a full navigation stack (semantic world model, A* planner, tour executor, path follower) and integrating SLAM (gmapping) plus a hybrid reactive safety controller. Experienced taking systems from Gazebo simulation to real hardware, including extensive real-world debugging and Docker-based development to handle ROS/Ubuntu version constraints; planning a move to ROS 2 on Turtlebot 4.”