Vetted Linux Professionals

“Backend/ML integration engineer with experience at Accenture and Walmart building Flask-based analytics and prediction APIs on PostgreSQL/MySQL. Strong focus on performance and scalability—uses precomputed aggregates, Redis caching, query tuning (indexes/partitioning/EXPLAIN), and async/background processing; also designs secure multi-tenant isolation with JWT and schema/db-per-tenant strategies.”

“Software engineer who built a reusable React component package (UI modules, auth helpers, API client wrappers) for an AI SaaS background-removal project, emphasizing performance (tree shaking/dynamic imports) and reliability (Jest + Storybook). Also delivered a unified REST API for Samsung Big Data Portal, resolving cross-team issues by standardizing schemas, improving validation/logging, and operating effectively amid shifting requirements.”

“Robotics software engineer who led development of a vision-based end-effector stability/vibration analysis tool using phase-based motion magnification and frequency-domain analysis (FFT/Bode) to uncover resonances missed by motor-only diagnostics. Experienced with ROS 2 C++ perception/navigation (ArUco + PnP) and real-time industrial integration, including optimizing a 1 kHz EtherCAT/Beckhoff PLC/Modbus TCP diagnostic pipeline and designing deterministic interfaces across heterogeneous subsystems.”

“Space/heliophysics ML engineer who built a PyTorch GRU model to propagate solar wind from L1 to the magnetopause with probabilistic outputs for uncertainty quantification, achieving ~25% better CRPS than standard approaches. Also developed production-grade Python ETL and an open-source telemetry processing package for a mission (LEXI), using Docker and GitHub Actions CI/CD and iterating with scientist/engineer stakeholders.”

“Controls and automated-vehicle engineer from the EcoCar EV Challenge who led an 8-person team implementing ACC and lane-centering directly on a vehicle (1st place result). Strong in CAN-based debugging, simulation-to-real deployment (Simulink to C++/dSPACE), and distributed robotics communication using DDS, with additional exposure to multi-agent RL and control barrier functions for coordinated driving.”

“Backend/Python engineer who owned an end-to-end FastAPI + AWS internal natural-language document Q&A system (Textract extraction, embeddings/vector DB, LLM integration) with strong focus on reliability and latency. Hands-on with Kubernetes + GitOps (Argo CD, Helm, rolling updates/auto-rollback) and built/optimized Kafka streaming pipelines using Prometheus/Grafana. Also supported a zero-downtime on-prem to cloud migration with parallel run and gradual traffic cutover.”

“QA automation engineer with Barclays digital banking experience who owned an end-to-end regression suite across UI, API, and database layers (Selenium/TestNG, REST Assured, SQL) and integrated it into CI/CD (Jenkins/GitLab). Known for preventing high-impact financial defects like duplicate transaction postings by adding backend SQL validations, negative/edge-case coverage, and converting production issues into automated regression tests; also strong in Cypress flake reduction using cy.intercept/cy.session and stable selectors.”

“AI/LLM engineer who built a production QA RAG for a University of Massachusetts faculty success initiative, cutting service tickets by 70%. Strong end-to-end RAG implementation skills (LangChain, Qdrant, hybrid/HyDE retrieval, FastAPI) with rigorous evaluation (RAGAS, LLM-as-judge) and practical handling of constraints like API rate limits and cost. Prior cross-functional delivery experience collaborating with SMEs and business owners at TCS and IBM.”

“Software engineer with experience at Goldman Sachs and Arizona State University’s Learning Engineering Institute, shipping production backend systems including a vendor equities invoice-generation service designed for extensibility across multiple vendors. Built Django REST + PostgreSQL backends with JWT auth and Pytest coverage, and delivered data-heavy, responsive Angular dashboards; also has exposure to AWS EC2 deployments and GitLab CI/CD automation.”

“Backend/API engineer with ~4 years experience building production services in .NET Core/PostgreSQL/Redis/Docker and optimizing real-world latency issues (claims ~60% response-time improvement). Also built and owned an end-to-end RAG-based AI assistant using Python/FastAPI, OpenAI APIs, and Pinecone, plus agentic workflows with reliability guardrails (retries, confidence thresholds, monitoring). Currently pursuing a master’s degree and targeting a $150k base salary.”

“Data engineer/backend data services practitioner with Bank of America experience building real-time and batch transaction-monitoring pipelines and APIs (Kafka + databases, REST/GraphQL). Highlights include a reported 45% response-time improvement through performance optimizations and use of Delta Lake schema evolution plus CI/CD (GitHub Actions/Jenkins) and operational reliability patterns like CloudWatch monitoring and dead-letter queues.”

“Data engineer focused on building reliable, production-grade pipelines and external data collection systems on AWS (S3/Lambda/SQS/Glue/EMR) using PySpark/SQL, serving curated datasets to Snowflake/Redshift for finance and fraud teams. Has operated a large-scale crawler ingesting millions of records/day with anti-bot tactics, schema versioning/quarantine, and CloudWatch/Datadog monitoring, and also shipped a versioned REST API with caching and query optimization.”

“Built and deployed an LLM-powered customer support assistant (“Notable Assistant”) focused on automating common post-customer queries while maintaining multi-turn context and meeting scalability/latency needs. Experienced with production orchestration and operations using Kubernetes and Apache Airflow (DAG-based ETL, scheduling, monitoring/alerts), and has partnered closely with customer service stakeholders to align chatbot behavior with brand voice through iterative testing.”

“Engineering leader focused on developer platforms and open-source frameworks/SDKs, with strong community and release-engineering chops. Drove major reliability and DX improvements (30–50% faster release cycles; 2–3x repeat contributors; ~50% faster onboarding) and led an incremental Python monolith to TypeScript event-driven migration using Protobuf contracts, feature flags, and a plugin architecture to preserve backward compatibility.”