Find the engineer who fits the work.

Cloud and platform initiatives that need hands-on modernization of CI/CD, AWS or Azure infrastructure, Kubernetes-oriented delivery, monitoring, and operational documentation for distributed systems.

Best suited for senior platform, SRE, or infrastructure architecture work where production reliability, infrastructure automation, secure deployment practices, incident coordination, and stakeholder-facing technical tradeoff communication are central.

Automated QA role for a backend-heavy web or microservices product requiring Java, Kotlin or C# test automation, SQL validation, API testing, regression coverage, and CI-based test execution.

Frontend implementation roles involving React or React Native applications, complex dashboards, transactional workflows, real-time updates, state management, and integrations with backend APIs.

Backend implementation and enhancement of .NET-based enterprise platforms that require C#, ASP.NET Core, SQL database work, REST APIs, codebase investigation, bug fixing, and incremental feature delivery.

Frontend engineering role for a complex B2B web platform that needs React implementation, reusable component patterns, codebase modernization, testing improvements, and pragmatic collaboration with product and backend stakeholders.

Frontend implementation for customer portals, backoffice interfaces, and cross-platform mobile applications using React, React Native, TypeScript, API integrations, and component libraries.

Building and productizing AI-enabled internal tools, automation platforms, conversational applications, or workflow assistants where requirements need to be clarified, LLM APIs integrated, backend and frontend components implemented, and the system deployed into a working environment.

Technical Product Manager for an AI-enabled B2B SaaS platform requiring roadmap ownership, requirements translation, agentic workflow design, RAG quality loops, API/integration design, stakeholder alignment, and close collaboration with engineering through QA and rollout.

Leading a B2B AI workflow from problem definition and MVP scope to a production release with evaluation and post-launch measurement.

Best suited for QA roles on web or mobile platforms where detailed functional testing, structured test cases, API validation, database checks, and close collaboration with product and engineering teams are needed.

Designing or improving B2B and B2C web/mobile interfaces where product flows, wireframes, prototypes, reusable components, and developer handoff need to be structured and visually consistent.

Best suited for B2B SaaS product work where the role combines customer discovery, requirements decomposition, product analytics, roadmap execution, cross-functional coordination, and clear documentation for engineering and QA teams.

Best suited for cloud platform roles that require building and improving CI/CD pipelines, automating infrastructure with Terraform, supporting Kubernetes workloads, and implementing monitoring and delivery practices across Azure, GCP, or AWS.

Best suited for building and improving NLP or multimodal ML services that combine transformer models, data processing pipelines, backend APIs, experiment tracking, and production-oriented deployment.

Backend development or backend technical-lead work on SaaS, fintech, healthcare, marketing, or data-oriented business platforms that need Node.js/NestJS services, PostgreSQL-backed APIs, third-party integrations, and coordination with frontend and product-facing stakeholders.

A quality engineering role focused on building and maintaining automated UI and API regression coverage for complex web platforms with business workflows, integrations, and structured data validation.

Applied ML engineering for NLP or classification systems, including dataset preparation, model training, evaluation harnesses, deployment support, monitoring, and iterative improvement in production-oriented environments.

Best suited for QA automation roles that combine hands-on test implementation with building test processes, improving test coverage, supporting regression suites, and mentoring QA engineers in product teams.

Backend implementation role on a PHP-based SaaS or business platform where the work combines feature delivery, service-layer design, refactoring, database interactions, and collaboration with product or business stakeholders.

Enterprise database engineering or DBA role focused on SQL Server operations, query optimization, high availability, disaster recovery, database migrations, and operational ETL support.

Backend implementation role for payment, lending, marketplace, or document-workflow platforms requiring TypeScript services, API contracts, asynchronous messaging, third-party integrations, cloud deployment support, and production maintenance.

Backend implementation role for Node.js and TypeScript services involving workflow automation, API design, database-backed business logic, microservices, integrations, and maintainable service architecture.

Building or modernizing B2B web applications with Node.js or .NET services, React-based interfaces, PostgreSQL-backed data flows, REST or GraphQL APIs, and third-party integrations.

Full-stack product work where a .NET Core backend, REST or minimal APIs, messaging, relational and document databases, and React or React Native interfaces need to be implemented and maintained within an existing engineering team.

Backend implementation for enterprise web platforms that need ASP.NET Core APIs, identity and access workflows, relational database work, service integration, and pragmatic maintenance of production codebases.

Building and supporting Azure-based enterprise analytics platforms that integrate multiple operational systems into governed data lake, warehouse, and BI-ready datasets.

Best suited for senior platform, SRE, or infrastructure automation work involving cloud modernization, Kubernetes-based deployments, Terraform-managed environments, CI/CD standardization, operational observability, and AI-assisted internal tooling.

Hybrid-cloud platform role focused on Kubernetes operations, CI/CD and infrastructure automation, MLOps workflow enablement, and engineering tooling for enterprise or communications platforms.

Data engineering and data architecture work for cloud-based analytical platforms that need reliable ETL/ELT pipelines, streaming ingestion, warehouse modeling, and ML-ready datasets.

Enterprise platform engineering role focused on cloud foundations, Kubernetes delivery standards, infrastructure as code, access governance, CI/CD enablement, monitoring, and support for engineering teams operating production workloads.

A small to mid-sized product team needing a QA specialist to combine manual exploratory testing, Playwright/TypeScript automation, regression coverage, test management, and release-quality workflow improvements.

Frontend ownership of complex React and TypeScript modules in production web platforms, especially real-time dashboards, alerting workflows, customer-service workspaces, analytics interfaces, and reusable component libraries.

Security operations engineering roles focused on building or improving SOC capabilities, integrating telemetry from endpoint, network, cloud, and identity systems, tuning detections, automating alert triage, and supporting compliance-driven response workflows.

Security Center or enterprise security operations role focused on incident triage, vulnerability follow-up, SIEM/log analysis, password-security rollout coordination, access-control processes, and compliance support.

Best suited for a managed security operations or enterprise SOC role focused on SIEM administration, customer onboarding, detection configuration, vulnerability management, incident escalation, audit support, and security platform operations.

Designing and evolving AI-enabled B2B or B2C products where complex user flows, data visualization, onboarding, design systems, and close collaboration with product, engineering, domain experts, and stakeholders are central.

Production platform engineering for cloud-hosted microservices: container-platform migration, Kubernetes/ECS operations, Terraform-based infrastructure, GitOps delivery, observability, and secure deployment controls.

AI-enabled SaaS or internal product initiatives that need a hands-on engineer to clarify product intent, design an MVP-oriented architecture, integrate LLM APIs, build full-stack features, and bring quality practices into the codebase.

Backend or full-stack-leaning backend role involving service decomposition, platform refactoring, API integration, operational tooling, and product-facing architecture for production systems.

Best suited for senior platform engineering or SRE work involving infrastructure as code, cloud operations, Kubernetes migration, reliability practices, observability modernization, and security automation in complex engineering environments.

Backend-heavy product platform work involving Symfony services, high-load workflows, monetization or experimentation infrastructure, semantic search integration, release stabilization, and production-safe migrations.

Building AI-native web platforms, LLM-powered generation workflows, developer tooling, MCP integrations, and code-export pipelines where TypeScript, Node.js, Next.js, retrieval, validation, and production delivery all matter.

Best suited for a hybrid technical product role where discovery, requirements, roadmap and backlog ownership, API/data platform coordination, client-facing clarification, and delivery governance need to stay closely connected.

B2B product and technical delivery roles that combine discovery, workflow design, AI API integrations, automation orchestration, and coordination of MVP-to-production workflows for operational SaaS or CRM systems.

Leading AI discovery and transformation initiatives for B2B organizations, defining practical use cases, coordinating product and engineering delivery, and shaping RAG, conversational automation, document intelligence, and agentic workflow solutions.

Best suited for leading AI-enabled knowledge, document intelligence, decision-support, and enterprise integration initiatives where business requirements, security controls, human oversight, and operational adoption must be coordinated across technical and domain teams.

Building AI-first SaaS features that combine LLM workflows, backend APIs, frontend product flows, asynchronous processing, cloud deployment support, and stakeholder feedback loops from MVP to production-oriented rollout.

Best suited for building or extending production LLM features that connect natural-language interfaces with enterprise data warehouses, APIs, validation pipelines, and operational monitoring.

Leading discovery, scoping, API and data contract design, delivery coordination, and launch readiness for B2B SaaS, internal platform, live-operations, or AI-enabled workflow initiatives.

Building production AI diagnostic or automation systems that combine LLM APIs, retrieval, structured outputs, data pipelines, backend services, release controls, and stakeholder-facing operational workflows.

Building and iterating AI-enabled web products where discovery, requirements shaping, LLM/RAG integration, full-stack implementation, release preparation, and practical validation are handled close to the business problem.

Frontend leadership or senior hands-on frontend role in a product-oriented web platform, especially where the team needs architecture ownership, delivery decomposition, quality practices, automated testing, and structured use of AI-assisted development tools.

Technical product ownership for AI-assisted automation, analytics, API/SDK, or internal developer-tooling products where the role bridges business discovery, engineering collaboration, user research, backlog shaping, and measurable rollout planning.

Technical Product Manager or Technical Product Owner for B2B SaaS, platform adoption, API standards, compliance validation, or AI-enabled workflow initiatives where product discovery, backlog ownership, stakeholder alignment, and engineering coordination are central.

Best suited for leading AI product discovery, MVP shaping, roadmap prioritization, and delivery of production LLM or ML platform initiatives that require close coordination between customers, engineers, model operations, and business stakeholders.

Technical product delivery for B2B or enterprise web applications where the role requires turning stakeholder goals into scoped iterations, user stories, acceptance criteria, implementation plans, and tested software increments.

Technical product ownership for AI-enabled operational platforms, marketplace fulfillment systems, partner API integrations, or internal productivity tools where product discovery, systems thinking, delivery coordination, and prototype validation are needed.

iOS product engineering for fintech, HRTech, or SaaS applications requiring Swift-based feature development, mobile architecture, reusable UI components, code review, and collaboration with stakeholders on scope and delivery priorities.

Best suited for Unity multiplayer products that need gameplay implementation, networking architecture, backend/API integration, performance optimization, and practical AI-assisted engineering workflows.

Best fit for AI product initiatives that need a lead who can translate business needs into an MVP, define acceptance criteria, coordinate cross-functional delivery, and guide implementation of LLM, RAG, ASR, evaluation, and analytics workflows.

Building or evolving a production SaaS platform that integrates LLM, speech-to-text, text-to-speech, retrieval-based support workflows, customer integrations, and observability-backed backend services.

Best suited for early-stage or transformation-focused AI initiatives that need a hands-on leader to shape an agentic AI product concept, design the architecture, prototype core workflows, and coordinate implementation across product and engineering concerns.

Best suited for leading discovery-to-MVP work for AI-enabled products, validating hypotheses, coordinating product and engineering teams, and introducing LLM, computer-vision, recommendation, or automation capabilities into existing digital products.

Technical product ownership for a B2B SaaS platform where complex workflows, integrations, AI-enabled internal tooling, rollout coordination, and engineering-facing requirements need to be aligned across product and technical teams.

Best suited for QA automation roles that combine API test automation, mobile automation, regression coverage, manual validation, defect tracking, and collaboration with development teams on production-oriented business systems.

Best suited for leading discovery-to-delivery of B2B AI transformation initiatives where LLM workflows, RAG, compliance-aware architecture, stakeholder alignment, unit economics, and production readiness must be coordinated across product and engineering teams.

Best suited for a full-cycle B2B AI transformation or AI assistant platform role where product direction, stakeholder translation, architecture, hands-on backend implementation, RAG integration, delivery control, and customer-facing technical solution work are combined.

Technical Product Manager for a B2B SaaS or data-heavy platform that needs roadmap ownership, requirements validation, analytics instrumentation, stakeholder coordination, and close collaboration with engineering on APIs, microservices, reporting, and AI-assisted delivery workflows.

Leading an AI-enabled product or transformation initiative where product discovery, architecture, team coordination, implementation oversight, and production delivery need to be connected end to end.

B2B AI integration work where business workflows need to be analyzed, decomposed, validated with users or stakeholders, and implemented as LLM, RAG, document-processing, or assistant-style systems.

Best suited for teams building or modernizing QA automation around web, API, and mobile products, especially where AI-assisted test generation, LLM output evaluation, visual checks, and regression automation need to be introduced into an existing delivery workflow.

Leading a QA function while implementing automated smoke and regression tests and establishing load-testing practices.

Best suited for leading test automation strategy and implementation for enterprise web, API, data-heavy, payment, healthcare, or AI-assisted decision-support systems where quality frameworks, regression automation, and LLM response validation are central.

Implementing and maintaining Go services for a financial application with transaction flows, asynchronous processing and operational monitoring.

Backend implementation and technical leadership for marketplace, fintech, retail, or platform systems requiring Go services, service decomposition, messaging, database design, cloud deployment, and code review practices.

Best fit for a backend-heavy platform role involving Go microservices, event-driven processing, Kubernetes deployments, production observability, CI/CD automation, and integration of LLM/RAG capabilities into existing products.

Best suited for roles that require end-to-end solution design, event-driven integration architecture, technical decision documentation, and coordination across multiple engineering teams in complex platform environments.

Backend implementation or technical leadership for transaction-processing platforms, distributed microservices, legacy-service migration, and backend components for AI-enabled applications where the role includes architecture, hands-on implementation, and team guidance.

Implementing and maintaining cloud ingestion and transformation workflows, with reconciliation and curated datasets for analytics.

Modernizing and operating cloud analytics platforms with batch or near-real-time ingestion, governed data layers, analytical models, reporting enablement, and production support.

Best suited for enterprise data platform initiatives involving cloud lakehouse or warehouse engineering, upstream system onboarding, batch or streaming ingestion, PySpark transformations, data-quality controls, and release automation.

Best suited for a data platform or risk-data engineering role that needs a hands-on engineer to design ingestion, marts, monitoring, and ML scoring data flows while coordinating delivery across data, ML, backend, and analytics teams.

Building and maintaining Go-based backend platforms that integrate multiple LLM providers, asynchronous workers, semantic search, monitoring, and CI/CD delivery for SaaS or data-heavy products.

Backend leadership or senior individual-contributor role building high-reliability Go services, consensus-critical infrastructure, cross-chain systems, or LLM-backed backend platforms with production release ownership.

Backend-heavy platform or product engineering role involving Java microservices, API gateway work, cloud deployment collaboration, operational monitoring, and integration with data, frontend, product, and QA teams.

Backend implementation role for Go/PHP microservices, API platforms, service extraction from monoliths, event-driven integrations, logging, and production feature delivery.

Best suited for a senior hands-on data engineering or data architecture assignment involving cloud lakehouse migration, medallion-style data layers, batch and streaming pipelines, governance, lineage, data quality, and CI/CD-enabled delivery.

Backend roles building high-throughput event-driven services, gRPC/REST APIs, Kafka-based processing, multi-tenant platform components, and production observability in cloud or Kubernetes environments.

Best fit for building or extending enterprise AI automation platforms with LLM workflows, RAG, persistent memory, evaluation loops, and cloud-deployed Python services.

Best suited for building enterprise AI systems that combine retrieval, document ingestion, LLM evaluation, workflow automation, API integration, cloud deployment, and observability.