Pre-screened and vetted.
Senior Software Engineer specializing in backend systems, AI/LLM integration, and cloud infrastructure
“Backend engineer with experience in highly regulated and high-stakes systems, including an airline crew messaging platform requiring near-zero-error real-time operations and a HIPAA-compliant mental health application built from an early-stage concept. They also show strong operational maturity, having owned a GoDaddy production incident through resolution and then led deployment pipeline improvements that reduced build failures by 40% and doubled deployment frequency.”
Entry-level Full-Stack Software Engineer specializing in FinTech and web applications
“Built end-to-end internal and user-facing automation/data features, including a Selenium-based BU course scraper with around 1,800 users and a CSV export system that became the company standard at Triple. Shows initiative in ambiguous environments, working directly with business stakeholders and resolving production infrastructure issues involving AWS and Terraform.”
Senior Operations Analyst specializing in business intelligence and financial services
“Analytics-focused candidate with hands-on experience turning messy datasets into reporting-ready outputs using SQL, building reproducible Python workflows, and operationalizing metrics in R Shiny dashboards. They stand out for combining structured data analysis with NLP and segmentation in marketplace-style datasets such as Airbnb, real estate, and sports salary data to drive pricing, engagement, and demand insights.”
Junior Software Engineer specializing in AI, data, and full-stack applications
“Builder with a mix of backend engineering, product instinct, and startup execution: they shipped a legal BI platform from scratch that handled 1,000+ cases, cut reporting time 80%, and saved $30K annually. They also move quickly in ambiguous environments, from launching a roommate app across iOS/Android after user discovery to building a RAG system with a 50+ case evaluation suite and a cloud dev environment in under 48 hours.”
Mid-level AI Engineer specializing in LLMs, RAG, and production ML systems
“Backend engineer who built an AI-powered grant matchmaking platform for researchers and professors, combining semantic matching, embeddings, and Semantic Scholar enrichment with rule-based eligibility filters. Stands out for pragmatic AI engineering: they focused on reliability through confidence scoring, logging, manual validation, and production-minded backend design.”
Senior Front-End Engineer specializing in enterprise UI architecture for FinTech
“Senior frontend engineer with 6 years of experience building enterprise-grade Angular/React applications in regulated industries, especially banking. Most notably worked on BLCM at Tasha Bank, a compliance platform that replaced fragmented Word/Excel SharePoint processes with a unified browser-based workflow for analysts and managers, and drove measurable performance and usability gains.”
Mid Software Engineer specializing in Python backend systems for FinTech
“Full-stack Python engineer who has owned internal automation products from requirements through production, including a financial reporting platform that improved deployment time by 45% and raised reporting efficiency to 98%. Also built an AI-powered movie recommendation engine using collaborative and content-based filtering, with hands-on experience across frontend, backend, data pipelines, and ML evaluation.”
Mid-level Full-Stack Software Engineer specializing in microservices and scalable backend systems
“Backend/microservices engineer (Java/Spring Boot, Kafka, Angular microfrontends) with Teradata experience building distributed analytics/query routing platforms and delivering 20–30% latency reductions through event-driven redesign and reliability hardening. Also built and shipped an end-to-end multimodal medical imaging AI feature (LLaVA/Mistral 7B + LoRA) with production guardrails like confidence-based human review, drift monitoring, and audit logs.”
Junior Full-Stack & ML Engineer specializing in research tooling and applied machine learning
“Full-stack engineer and ML assistant in UC Irvine’s CS department who deployed a lab project showcase platform and integrated on-demand execution of computational projects using Docker for isolation. Also built and optimized Linux cloud/cluster test automation for research, diagnosing RAM and network sync bottlenecks, and later led development of a Python-based predictive analytics tool for musicians using probabilistic graphical models and flexible data pipelines.”
Mid-level AI Engineer specializing in GenAI, LLM integration, and RAG pipelines
“Built and led deployment of an autonomous, self-correcting multi-agent knowledge retrieval and validation system at HCA Healthcare to reduce heavy manual research/validation in clinical/compliance documentation. Deeply focused on production reliability and cost—used LangGraph StateGraph orchestration plus ONNX/CUDA/quantization to cut GPU costs by 25%, and partnered with the Compliance VP using real-time contradiction-rate dashboards to hit a 40% automation goal without compromising compliance.”
Mid-level AI/ML Engineer specializing in cloud data engineering and GenAI
“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”
Mid-Level Software Engineer specializing in microservices and cloud-native systems
“Backend-leaning full-stack engineer with logistics domain experience (DHL) who shipped a real-time shipment status update system using Spring Boot + Kafka and a performance-tuned PostgreSQL tracking schema. Also has AWS production operations experience (ECS/Kubernetes, Jenkins CI/CD, Terraform/Ansible) and has handled peak-load incidents end-to-end by tracing Kafka lag to database bottlenecks and resolving via query/index optimization plus scaling.”
Senior QA Automation Engineer (SDET) specializing in mobile and API test automation
“QA engineer with 11+ years (nearly 12) in IT across financial, healthcare, and telecommunications domains, experienced partnering closely with developers throughout Agile delivery. Strong in building/maintaining living test plans, running smoke/regression on new builds, and driving triage with impact-based prioritization using Jira/TestRail/Confluence; targets $100k+ base with standard benefits.”
Mid-level QA Automation Engineer specializing in web, API, and CI/CD test automation
“QA automation engineer with hands-on ownership of Selenium (C#) and Cypress (JavaScript) suites, including CI integration in GitLab with PR smoke gating and nightly regressions with JUnit reports/screenshots. Drove a reported ~60% reduction in manual effort, improved suite maintainability through reuse/merging tests, and proactively shaped requirements/acceptance criteria in sprint planning to prevent defects (including claims calculation and server/log-related issues).”
Mid-level Software Engineer specializing in data pipelines and backend APIs
“Data engineer with Webster Bank experience owning end-to-end pipelines (APIs + databases) processing millions of records/day, improving data quality (25–30% fewer issues) and reliability (~99.9% successful runs). Built resilient external data ingestion/scraping systems (schema-change validation, idempotent backfills, monitoring/alerts) and shipped a FastAPI service exposing curated datasets with versioning and consistently low latency.”
Intern Full-Stack Software Engineer specializing in AI/ML and cloud
“Built a Python-based geospatial machine learning backend for PFAS contamination risk mapping, including reproducible feature pipelines, ensemble modeling, and a FastAPI layer for visualization/analysis. Emphasizes data integrity and robustness (CRS/coverage checks, fail-fast validation) and has led safe backend refactors using feature flags, idempotent backfills, and Postgres RLS for secure, queryable results delivery.”
Junior Software Engineer specializing in machine learning and data science
“Python backend engineer who built a personal LLM-powered AI code review tool that parses code into context-preserving diff chunks and uses the OpenAI API to analyze and summarize changes. Has hands-on Kubernetes deployment experience (replicas, rolling updates, ConfigMaps/Secrets, health probes) and follows GitOps-style, declarative CI/CD workflows; also has experience designing streaming/event-style processing with attention to reliability and observability.”
Senior Business Analytics Analyst specializing in product and customer analytics
“Darwinbox team member who supported talent/recruiting operations while also driving product improvements across HR modules (recruitment, onboarding, payroll, performance). Led a small team (5–6) and implemented discovery-driven configuration and BI reporting (Power BI/Tableau/Confluence), including a reported 30% reduction in recruitment configuration issues and real-time funnel reporting to support fast hiring.”
Mid-level AI/ML Engineer specializing in generative AI, RAG platforms, and LLM agents
“AI/LLM engineer who has shipped 10+ production applications, including InvestIQ on GCP—a production-grade RAG due-diligence engine that ethically scrapes web/PDF sources, builds a ChromaDB knowledge base, and delivers analyst-style dashboards plus a citation-backed chat copilot. Deep focus on reliability (evidence-only answers, hard citations, refusal gating), retrieval tuning, and orchestration (Airflow/Cloud Composer), plus multi-agent systems (CrewAI with 7 specialized finance agents).”
Senior AI/ML Engineer specializing in production-grade LLM systems for regulated finance
“AI/LLM engineer with published work who built FinVet, a production financial misinformation detection system using multi-pipeline RAG, confidence-based voting, and evidence-backed outputs (F1 0.85, +37% vs baseline). Also built NexusForest-MCP, a Dockerized Model Context Protocol server exposing structured global deforestation/carbon data via SQL tools for reliable LLM tool use. Previously delivered borrower risk-rating (PD) models at BMO Financial Group that were validated and integrated into an enterprise credit system through close collaboration with credit officers and portfolio managers.”
Senior Data Engineer specializing in data infrastructure and marketing/CRM analytics
“Salesforce-focused implementation/solutions engineer from Full Circle Insights who owned end-to-end campaign attribution and reporting deployments for multiple customers at once (3–5 concurrently), including sandbox testing, KPI monitoring, and rollback-safe migrations from legacy reporting. Also builds personal multi-agent workflows and uses Claude Code to rapidly scaffold data/analytics scripts like an advertising optimization parser over CSV/XLSX inputs.”
Mid-level Data Analyst/Data Engineer specializing in BI, ETL pipelines, and cloud analytics
“Data engineer focused on marketing/web analytics and external API pipelines, handling ~10M records/week. Built Azure-based ingestion and PySpark transformations with rigorous data quality checks, then served curated datasets into Synapse/Redshift for Power BI. Also designed an Airflow-orchestrated crypto REST API pipeline with monitoring, retries/exponential backoff, schema-change detection, and backfill-friendly reprocessing.”
“Analytics-focused candidate with internship and project experience at Recotap and CoUnderscore, combining SQL, Python, and BI dashboards to turn messy marketing and engagement data into decision-ready reporting. Stands out for tying analytics work to business outcomes, including ~15% CTR improvement, identifying ~40% misattributed spend, and enabling a ~$75K budget shift through better targeting.”
“Analytics professional with Northern Trust experience focused on investment portfolio reconciliation and reporting. They combine SQL, Python, and Power BI to clean and validate high-volume financial data, automate manual processes, and align operations and accounting teams on shared metrics—driving roughly 20% improvement in reconciliation accuracy.”