Vetted Shell Scripting Professionals

“Built and deployed a production Task Prioritization App using Python/Streamlit/MongoDB with Gemini API to score and rank tasks by context (deadlines, dependencies, urgency). Focused on reliability challenges like prompt tuning for nuanced task understanding, concurrent DB updates, and performance via async LLM calls, and validated usability through iterative feedback with a non-technical end user.”

“Built a production, real-time insurance claims document-understanding and fraud-detection pipeline using TensorFlow + fine-tuned BERT, deployed on AWS (SageMaker/Lambda/API Gateway) with automated retraining via MLflow and Jenkins. Addressed noisy documents and latency using augmentation and model distillation (3x faster), cutting claims ops manual review by ~50% and reducing fraudulent payouts.”

“Backend/ML platform engineer who owns end-to-end delivery of ML-serving APIs (FastAPI + TensorFlow) and runs them reliably on Kubernetes using ArgoCD GitOps. Has hands-on experience solving production-only issues (probe tuning for model warm-up, resource profiling) and building scalable Kafka streaming pipelines, plus supporting phased on-prem to AWS migrations with dependency discovery and recreation of hidden jobs/workflows.”

“Backend-leaning full-stack engineer from Valcom who maintained Django/PostgreSQL services for mass-notification/paging servers used by education/commercial/government customers. Notable for hardening authentication and reliability (JWT refresh + rotation tokens, WebSockets/EventStream) and for modernizing a legacy Ext.js/PHP stack into a React + Django architecture with strong CI/testing and production incident ownership.”

“Backend/platform engineer with hands-on ownership of Kubernetes GitOps delivery (GitHub Actions + Argo CD) on AWS EKS, including progressive rollouts and reliable rollback across interdependent microservices. Built a Python/FastAPI ML-driven document-processing service (PostgreSQL + S3) to complement existing Spring Boot systems, and implemented Kafka streaming pipelines with Schema Registry plus Prometheus/Grafana observability. Also supported a hybrid cloud-to-on-prem migration for compliance/latency with phased rollout and incremental PostgreSQL migration.”

“Backend/DevOps-leaning engineer managing multiple customer service platforms end-to-end (requirements through deployment). Built an in-house Python monitoring/alerting solution for Salesforce-to-Java contact sync jobs (Snowflake dependencies) that increased uptime ~60%, and helped modernize delivery by moving the team from manual releases to automated Jenkins-based deployments while coordinating an Oracle EBS→Fusion transition with business/data/IT stakeholders.”

“Built and shipped “LabMate,” a production AI assistant specialized in laboratory hardware, using a weighted multi-source RAG pipeline with reranking and reasoning-focused query decomposition to handle complex user questions. Deployed on a local GPU cluster with vLLM and NVIDIA MPS (plus OCR/VLM components), and established evaluation using synthetic + public reasoning datasets while collaborating weekly with non-technical admins to align requirements and resource constraints.”

“AI software engineer with experience spanning LLM/RAG production systems and regulated fintech infrastructure. Built an end-to-end natural-language-to-SQL analytics assistant (Weaviate + GPT-4 + Supabase) shipped as an API with 92% accuracy and major time savings for non-technical users, and also owned demand-forecasting and CI/CD/containerization improvements for a Bank of America core banking deployment at Infosys.”

“Backend/data platform engineer who owned a FastAPI + Kafka microservice in Verizon’s billing pipeline, handling high-volume usage ingestion/validation/enrichment with strong observability and CI/CD on AWS EKS. Demonstrated measurable performance gains (latency down to ~120–150ms; Kafka throughput +30–40%; DB CPU -25%) and led an on-prem ETL-to-AWS migration using Terraform, parallel validation, and phased cutover with zero downtime.”

“Built and deployed a production Llama 3-based RAG document Q&A system using FAISS, addressing context-window limits through chunking and keeping retrieval accurate by regularly refreshing embeddings. Has hands-on orchestration experience with LangChain and LlamaIndex for multi-step LLM workflows (including memory management) and collaborates with non-technical teams (e.g., marketing) to deliver AI solutions like recommendation systems.”

“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.”

“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.”

“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.”

“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.”

“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.”

“DevOps/Infrastructure engineer with hands-on production experience building Jenkins CI/CD pipelines that provision Kubernetes infrastructure and process data into a MapR cluster. Uses Terraform to provision AWS resources (EC2, S3, VPC, subnets) with remote state in S3, separate environment state files, and code review/validation practices; targeting $135k base.”

“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and 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.”

“Frontend/product engineer who has led end-to-end builds across automotive and healthcare: created a multi-tenant, high-performance Next.js luxury inventory platform and a secure, Stripe-powered sick-note workflow integrated with an EMR. Known for data-driven UX decisions (A/B testing) and pragmatic modernization of critical systems (3DS2 upgrade) with measurable conversion and risk improvements.”

“Backend engineer who owned an order management API built with Python/FastAPI and PostgreSQL, integrating payment and shipping providers with strong reliability patterns (idempotency, async workers, retries/backoff, circuit breakers). Experienced deploying services to Kubernetes using a GitOps model with ArgoCD (auto-sync, self-healing, pruning, rollbacks) and building high-volume Kafka streaming pipelines. Has also supported phased cloud-to-on-prem migrations with a focus on security monitoring/SIEM log continuity.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”

“Software engineer who built and open-sourced reusable React/Node.js modules (chat, auth, caching) from an AI education platform, emphasizing intuitive APIs and strong documentation. At TCS, improved a healthcare scheduling platform by diagnosing SQL/server bottlenecks and redesigning database + caching, cutting appointment load times by ~40% and reducing support complaints.”