Vetted Bash Professionals

“Backend/ML-focused engineer with experience spanning fintech, sales enablement, and medtech, including a Capital One capstone and a Singapore medtech startup internship. Stands out for owning end-to-end AI/backend systems, from a GenAI sales pitch platform that cut prep time by 50% to an ultrasound-guidance MVP for non-expert operators in a highly ambiguous domain.”

“AI/ML-focused engineer with practical experience building RAG-based and multi-agent systems, including architectures for retrieval, reasoning, context processing, and response generation. Stands out for combining LLM productivity gains with disciplined software engineering practices like validation, monitoring, and reproducibility.”

“AI/ML-focused software engineer who has built and orchestrated multi-agent systems with separate retrieval, planning, validation, execution, and escalation components. Stands out for combining hands-on experimentation with a strong reliability mindset, using observability, structured logging, tracing, and evaluation to make agentic workflows production-ready.”

“AIX/IBM Power Systems engineer with hands-on production incident leadership in a regulated banking environment, using deep OS-level tooling to diagnose CPU entitlement and memory pressure issues. Experienced with HMC/vHMC, VIOS, and zero-downtime DLPAR resizing, plus PowerHA/HACMP clustering and validated failover testing. Also drives migration readiness via Bash/Python automation (60% manual-effort reduction) and phased AIX cloud/hybrid cutovers.”

“Robotics software engineer with autonomous driving lab experience at UCSD, building and optimizing ROS2 perception and control pipelines (camera-based real-time object detection) with a strong focus on low-latency performance and robust message interfaces. Also brings production deployment experience from Hewlett Packard Enterprise, using Docker and Kubernetes for containerized environments and deployment pipelines.”

“Backend/founding-engineer-style builder who designed and evolved a near-real-time customer churn prediction platform (FastAPI + AWS SageMaker/Lambda + Redis + MLflow) to enable real-time retention actions, reporting ~18% churn reduction. Demonstrates strong production engineering in secure API design, incremental migrations with data integrity safeguards, and robustness improvements in async pipelines (idempotency, DLQs, retry visibility).”

“Robotics engineer with a 2024 internship building an end-to-end software stack for an autonomous humanoid robot that follows natural-language audio commands to make coffee and deliver snacks, including perception (OpenCV), mapping, and ROS Navigation. Also contributing to a robotics foundation model effort by building data preprocessing pipelines using GroundingDINO and SAM2, and has multi-robot coordination experience with algorithms designed to handle real-world communication drops.”

“Robotics engineer/team lead who built an autonomous lunar rover for the NASA Lunabotics challenge, owning electrical systems, comms (UART/WiFi), and autonomy using RealSense + custom YOLO on a Jetson Orin Nano. Also has extensive ROS 2 experience, including creating a ROS2+Gazebo environment with ros2_control custom controllers for a large-scale additive manufacturing printer and research/thesis work in multi-robot coordination and robustness.”

“Built and shipped a production LLM/RAG risk-case summarization and triage system used by fraud/compliance analysts, with strong grounding controls (evidence-cited outputs and refusal on low confidence). Demonstrates end-to-end ownership across retrieval quality, Airflow-orchestrated indexing pipelines, and compliance-grade privacy (PII redaction, RBAC, encrypted redacted logging, and auditable prompt/model versioning) plus a tight feedback loop with non-technical domain experts.”

“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”

“Built and deployed a production LLM/RAG chatbot at Wells Fargo for securely querying regulated financial and compliance documents, emphasizing low hallucination rates, explainability, and strict governance. Experienced with LangChain multi-agent orchestration plus Airflow/Prefect pipelines for ingestion, embeddings, evaluation, and retraining, and partnered closely with compliance/operations to drive adoption through demos and feedback-driven retrieval rules.”

“Built and deployed a production LLM/RAG knowledge assistant integrating internal docs, wikis, and ticket histories to reduce tribal-knowledge dependency and repetitive questions. Emphasizes reliability via grounding + a validation layer, and achieved major latency gains (>50%) through vector index optimization, caching, quantization, and selective re-validation. Comfortable orchestrating end-to-end LLM/data workflows with Airflow, Prefect, and Dagster, including monitoring and alerting.”

“Red Hat ML/LLM engineer who designed and deployed a production LLM-powered customer support automation system using RAG, improving latency by 30% via PEFT and vector search optimization. Built security and governance into retrieval (access-level filtering, encrypted Pinecone/ChromaDB) and delivered SHAP-based explainability via a dashboard for non-technical stakeholders. Experienced orchestrating distributed ML/RAG pipelines across AWS SageMaker and OpenShift with Airflow/Prefect, plus multi-agent workflows using CrewAI and LangGraph.”

“Platform-focused engineer experienced in productionizing ML/LLM systems: containerized a local prototype, implemented CI/CD, deployed to Kubernetes with scaling controls, and added monitoring/logging. Comfortable diagnosing real-time issues in LLM/agent workflows using logs/metrics and incident stabilization tactics, and supports sales calls by clearly explaining production scalability to unblock customer decisions.”

“QA professional with Apple experience supporting the macOS Sonoma release, owning daily monitoring of submissions, crashes, regressions, and release verification across multiple OS/device builds. Notably isolated a memory-related issue in the Safari suite that caused an 18-hour build backlog and 156+ test crashes, leading to a Python-script fix that restored build times to ~1 hour and helped keep the release on schedule.”

“Infrastructure reliability/capacity-focused engineer with hands-on IBM Power/AIX (LPAR/DLPAR, HMC, VIOS) performance troubleshooting and modern cloud-native delivery experience. Built production CI/CD and Terraform-managed AWS/EKS environments, and has led real incident recoveries spanning Kubernetes autoscaling and AWS quota constraints with concrete RCA and prevention improvements.”

“Data engineer with experience at Sony and Walmart building high-volume, near-real-time analytics and ingestion systems. Has owned end-to-end pipelines from Kafka/Spark streaming through S3/Parquet and Redshift/Looker, emphasizing data quality (Great Expectations), observability (CloudWatch/Azure Monitor), and reliability (Airflow SLAs, retries, checkpointing), including measurable performance and latency improvements.”

“Data engineer focused on fraud and banking analytics who has owned end-to-end batch + streaming pipelines at very large scale (hundreds of millions of records/day). Built robust data quality/observability layers (schema validation, anomaly detection, alerting) and delivered low-latency serving via AWS Lambda/API Gateway with DynamoDB + Redis, plus external data ingestion/scraping pipelines orchestrated in Airflow with anti-bot protections.”

Ajay Madhusudhan Thumala

Screened

Junior Software Engineer specializing in data engineering and LLM applications

Irvine, CA1y exp

“Computer science engineer and master’s graduate who independently built a mechatronics-heavy capstone prototype: a smartphone concept for deafblind users using micro-actuator arrays for braille reading. Also has platform engineering experience at Quantiphi, deploying webhooks to Kubernetes and implementing GitOps-based CI/CD using AWS CodeCommit/CodeBuild and ECR.”

Barath Kumar Jayachandran Kanchanamalini

Screened

Junior Robotics & Controls Engineer specializing in UAV autonomy and embedded systems

New York, NY1y exp

“Robotics software engineer focused on autonomous drones and mobile robotics: implemented a sliding mode inner-loop controller and a RealSense T265 VIO state-estimation pipeline integrated into ArduPilot EKF3 for GPS-denied indoor flight. Strong simulation-to-deployment experience (Gazebo/MAVROS to firmware), ROS2 networking/debugging, and hands-on validation through multi-sensor trials and log analysis.”

Aditya Jhaveri

Screened

Mid-level Software Engineer specializing in AI, big data, and distributed systems

Jersey City, NJ3y exp

“Software Developer at NYU (GEMSS) focused on scaling and optimizing a data-heavy asset management web app, including migrating/optimizing data access via Google Sheets API and Firestore. Previously an SDE at Sainapse working on Spring Boot microservices POCs (Kafka, Hadoop at 2B+ record scale). Built an end-to-end Apple Wallet coupon generation/redemption system using PassKit + Google Apps Script with measurable ops impact (40% efficiency gain).”

Mitchell Samora

Screened

Senior Software Engineer specializing in game development and cross-platform apps

Duvall, WA19y exp

“Client Platform engineer with strong Unity/C# background who built a spec-driven code generation system bridging Unity C# to Kotlin and a React/JavaScript UI layer, reducing feature implementation overhead by hours. Has shipped/implemented networking layers for multiple games (including a mobile MOBA) spanning deterministic action-logging approaches and physics/state sync with latency compensation, and built desync detection to debug divergence. Also prototyped a shared VR/mobile app architecture at Meta (Supernatural) demonstrating a single codebase running on both.”

“Built and stabilized production data pipelines in clinical genomics, including integrating a qPCR step into Baylor Genetics' workflow with a focus on reliability, turnaround time, and reducing manual intervention. Also has hands-on LLM production experience, creating a Python/OpenAI-based translation evaluation pipeline that reduced manual review time by 70% and improved scoring consistency.”