Vetted CI/CD Professionals

“Backend/platform engineer from Dune Security with strong experience turning messy, fragmented workflows into reusable production systems. They’ve built a shared database abstraction layer, integrated multiple enterprise security platforms into a unified workflow, and shipped AWS Bedrock-powered security insight features with guardrails and human review.”

“Backend/AI engineer who has shipped an LLM-powered enterprise support-ticket agent at Comcast, building a production-grade microservices pipeline (FastAPI, SQS, Redis) with strong observability (OpenTelemetry/Splunk/Prometheus/Grafana) and reliability patterns (async, caching, circuit breakers, idempotency). Demonstrated quantified impact at scale—processing 10k+ tickets/day while improving response SLAs and routing accuracy through evaluation and human feedback loops.”

“Built and owned full-stack features for a real-time pricing and inventory management platform at PDR Technologies, spanning React/TypeScript frontend architecture, Node.js APIs, and PostgreSQL-backed data flows. Stands out for driving a shift to micro-frontends with Nx and Webpack Module Federation, improving team independence and release speed while also delivering measurable frontend performance gains.”

Mahima Mannava

Screened

Junior Software Engineer specializing in AI, LLM systems, and full-stack development

San Jose, CA3y exp

“AI/full-stack engineer who built a computer-usage agent end to end, including a split local/cloud architecture that used a vision LLM to drive real Chrome workflows while avoiding bot detection. Stands out for combining product-minded systems design, rigorous evals, and prompt iteration to achieve sub-2-second latency and a 20% reduction in automation failures in an early-stage environment.”

“Built and shipped an AI-powered support assistant at PNC Bank, owning both the React/TypeScript frontend and Python/FastAPI + LangChain/OpenAI backend. Stands out for applying RAG in a regulated banking environment with measurable impact: 34% better knowledge retrieval accuracy and 27% faster support lookup time.”

“Data/analytics-focused engineer who builds end-to-end KPI reporting and validation products used daily by plant leads and leadership to track yield, downtime, and defects. Combines Python/SQL + Power BI data pipelines with strong data-quality practices (automated validation, monitoring/alerts) and has experience designing scalable frontend architecture in TypeScript/React and working in distributed/microservices-style data systems.”

“Runs two lean real estate companies remotely by building local on-the-ground contact networks and leveraging free-tier technology to keep total annual business costs under $100. Brings a cost-elimination and MVP/validation-first mindset, preferring to join an established company unless a clearly viable business idea emerges.”

“Backend engineer with TaskRabbit experience building and operating payment/booking services in Python/Django on AWS (ECS + Lambda) with Kafka/SQS eventing. Demonstrates strong production reliability and incident ownership in high-stakes payment flows (idempotency, strict timeouts, retries, monitoring/alerting) plus data/ETL work in AWS Glue and measurable SQL performance wins.”

“Full-stack engineer who built an internal user-activity tracking and reporting system end-to-end using React/TypeScript, Node/Express, and Postgres, deployed on AWS (EC2/ALB, S3/CloudFront) with CloudWatch observability. Emphasizes reliability and data correctness via idempotent ingestion, retries with exponential backoff, backfills/reconciliation, and performance tuning as data scales, and has experience shipping quickly in ambiguous early-stage startup conditions.”

“Backend/data engineer with hands-on production experience across GCP and AWS: built FastAPI microservices on Cloud Run and delivered AWS Lambda + ECS Fargate systems with Terraform/GitHub Actions. Strong in data engineering (Glue/Spark, S3/Redshift) and modernization (SAS to Python/SQL), with proven reliability and incident ownership—including cutting a 20+ minute reporting query to under 2 minutes.”

“Unity VR game programmer with professional experience shipping a VR experience targeting Meta Quest and SteamVR, including prior VR tech QA experience at Rockstar. Served as the only core tech programmer on a team, owning physics/player controller/gameflow while building designer-friendly workflows so non-coders could create content. Also built a largely solo FPS project (Project Alpha) and later evolved it into a multiplayer game with a data-driven weapon system.”

“AI/LLM engineer who built a production autonomous GenAI content ecosystem that generates short-form scripts, extracts viral highlights from long-form video, and dubs content into 33+ languages. Focused on making LLM outputs production-safe via schema enforcement, token-to-time alignment, critic-agent verification, and scalable async orchestration—cutting manual workflows by ~90% and saving $200k+ annually.”

“Backend/data engineer with production experience building secure Django/DRF APIs (JWT RS256 + rotating refresh tokens), background processing with Celery, and strong reliability practices (timeouts, retries/backoff, structured logging, audit trails). Has delivered AWS solutions spanning Lambda + ECS with IaC/CI-CD and built Glue/PySpark ETL pipelines with schema evolution and data-quality quarantine patterns; also modernized a legacy SAS pipeline to Python/PySpark with parallel-run parity validation and phased rollout.”

“Built and shipped a production multi-agent conversational AI platform (Monitor agent + RAG + 4 additional agents) with enterprise REST APIs, using ChromaDB-grounded WCAG knowledge to keep responses accurate while varying tone via personality modes and conversation memory. Has experience at LinkedIn delivering technical demos and pre-sales guidance to both engineering teams and C-level stakeholders, acting as a translator between sales and technical teams to drive adoption.”

“AI/LLM engineer who built a production content-generation system for nursing education, combining multimodal RAG over proprietary PDFs (including images) with structured Cosmos DB data and external sources. Strong focus on production reliability—prompt-chaining with LangChain, validation/guardrails, and Azure-based monitoring/observability—plus experience designing Azure AI agents with tool integrations like Bing Search.”

“Built and productionized a multi-agent, LLM-powered document understanding system to replace manual review of long documents, using LangGraph orchestration plus RAG to reduce hallucinations. Implemented layered reliability controls (structured templates, checker agent, and human-in-the-loop feedback) and reported ~40% speed improvement after orchestration; also has hands-on Airflow experience for scheduled data pipelines.”

“LLM/agentic systems builder who has deployed multi-agent educational chatbots using LangChain + LangGraph, with LangFuse-based tracing and FastAPI hosting. Focused on production reliability and performance (latency reduction via agent decomposition and caching) and on evaluation/testing (routing test scenarios, LLM-as-judge). Partnered with product to add image understanding by parsing and storing images in S3, expanding chatbot coverage to 30+ books with images.”

“Built and deployed a production long-form article summarization system using BART/T5/PEGASUS, tackling real-world constraints like token limits, latency/quality tradeoffs, and factual drift via chunking/merge logic and constrained decoding. Uses pragmatic Python-based pipeline orchestration (scheduled jobs, modular scripts, logging/retries) and iterates with stakeholder feedback to make outputs genuinely useful for content workflows.”

“LLM/ML platform engineer with hands-on experience taking an LLM document summarization prototype into a production-grade service on AWS EKS, emphasizing low-latency inference, drift monitoring, and safe CI/CD rollouts (canary + rollback). Strong in real-time debugging of agentic/RAG systems (tracing, retrieval/index drift fixes) and in developer enablement through practical workshops (Docker/Kubernetes/FastAPI) plus pre-sales support via demos and benchmarks to close pilots.”

“Full-stack engineer who built a policy management and notifications platform end-to-end: Java/Spring Boot microservices with PostgreSQL plus a React/Redux UI, deployed on AWS with Docker and Jenkins CI/CD. Demonstrates strong real-world scaling and reliability practices (Redis caching, Kafka, query/index tuning, ACID transactions, locking, and idempotent processing) to handle high-volume concurrent workloads.”

“Product-focused full-stack engineer (70/30 app vs infra) with Accenture experience and recent AI workflow work, shipping end-to-end systems from React/TypeScript UIs through FastAPI backends to Postgres. Built an AI-driven data extraction platform with async job APIs, strict schema validation, and strong observability, and has operated AWS ECS-based deployments with real incident mitigation (DB connection exhaustion/latency under traffic spikes).”

“Open-source contributor who improved React Query’s caching/subscription behavior to reduce unnecessary re-renders via debouncing and batched updates, validated with benchmarking and extensive tests. Also maintained a Flask extension and resolved production background-task hangs by tracing Redis connection handling issues, adding cleanup/retry logic and troubleshooting docs. In a fast-paced startup, owned the design of a Celery+Redis multi-queue background processing system with Prometheus-based observability.”

“Backend/distributed-systems engineer (Amazon; also Bank of America) pivoting into robotics software. Built and owned an end-to-end cross-region event processing service for Aurora Global Databases, emphasizing correctness under latency/clock skew, fault tolerance, and strong observability; brings deep Docker/Kubernetes and CI/CD experience to robotics infrastructure and reliability work while ramping up on ROS 2.”

“Backend-focused Python/Flask engineer with strong performance and scalability experience across PostgreSQL/SQLAlchemy optimization, caching, and async processing. Has implemented multi-tenant data isolation (schema/db per tenant with RBAC and encryption) and integrated TensorFlow-based ML inference behind a Flask REST API using Redis caching, batching, and async execution; reports measurable wins like cutting endpoints from 6–8s to ~2s and increasing throughput 3–4x via Celery queues.”