Reference
Plain-English definitions of the staffing, managed-delivery, and specialty engineering terms our clients use every week. Definitions are written for buyers — short enough to scan, precise enough to be cited.
Also known as: technology staffing, tech staffing
A service in which a staffing firm places pre-vetted technical contractors on a client engineering team to fill specific role gaps. The contractor reports to the client's management and works alongside the client's full-time staff. IT staffing engagements typically run as fixed-term contracts, with optional contract-to-hire conversion paths.
Also known as: IT staff augmentation, team augmentation
An IT-staffing pattern where one or more outside engineers are added to an existing team — under the client's management — to accelerate a workstream or fill a skill gap. Distinct from outsourcing because the augmented engineer is embedded in the client's process, tooling, and standups.
Also known as: temp-to-perm, C2H
An engagement model that starts as a contract placement with a built-in path to permanent employment after a defined evaluation period. Used to reduce risk on full-time technical commitments — both parties confirm fit before converting.
Also known as: permanent placement, perm placement
A recruiting service in which a staffing partner sources, vets, and presents permanent employee candidates for a client's full-time technical role. The staffing firm is compensated via a placement fee — typically a percentage of first-year compensation (contingent) or a retainer (retained).
Also known as: managed IT services, MSP
An engagement in which a vendor assembles and manages a dedicated team that owns an ongoing operational scope — for example DevOps, platform operations, application support, or continuous delivery. The client pays a monthly retainer and gets a fully-managed team; internal engineering retains strategic ownership.
Also known as: project-based delivery, scoped delivery
A delivery model where a vendor scopes, staffs, and delivers a bounded technical outcome — cloud migration, platform build, ML stand-up — on a defined timeline with milestone-based or fixed-fee pricing. Differs from managed services in that it is time-bound, with explicit success criteria and handoff.
A practice combining software development and IT operations to shorten the systems-development life cycle while delivering features, fixes, and updates frequently and reliably. DevOps engineers build CI/CD pipelines, manage infrastructure-as-code, run observability, and own production reliability.
Also known as: SRE
A discipline that applies software-engineering practices to operations problems — defining service-level objectives, building observability, automating remediation, and treating reliability as a feature. SREs typically share an on-call rotation with product engineers and own the production environment.
The practice of building and operating an internal developer platform — the toolchain, services, and abstractions other engineers use to ship. Platform engineers reduce cognitive load on product teams by paving golden paths for deployment, observability, and security.
Also known as: machine learning operations
The set of practices for deploying, operating, and maintaining machine-learning systems in production — feature stores, model registries, serving infrastructure, monitoring for drift, and CI/CD for models. MLOps engineers bridge data science research and production reliability.
Also known as: applied AI engineer, generative AI engineer
An engineer who designs, builds, and operates applications powered by large language models or other generative AI — including prompt engineering, retrieval-augmented generation (RAG), agentic systems, fine-tuning, evaluation, safety, and inference cost optimization.
The process of moving applications, data, and infrastructure from on-premises systems or one cloud provider to another. Migrations range from lift-and-shift (rehost) through replatform to re-architecture, and require coordinated work across networking, identity, data, and CI/CD.
The discipline of designing, building, and operating the systems that collect, store, transform, and serve data — pipelines, warehouses, lakes, streaming systems, and the orchestration around them. Data engineers turn raw operational data into decision-quality data for analysts and ML systems.
Also known as: data pipeline, data integration
Extract-Transform-Load (or Extract-Load-Transform) is the pattern for moving data from source systems into an analytical store. ELT is the modern variant where transformation happens after load, inside the warehouse, often orchestrated with tools like dbt and Airflow.
Also known as: Software Development Engineer in Test, test automation engineer
An engineer who writes production-grade test automation — frameworks, integration tests, performance and resilience tests — and partners with developers on quality engineering. SDETs typically work in the same languages as the product team and own test infrastructure.
Also known as: embedded systems engineer, edge computing engineer
An engineer who builds connected devices and the systems supporting them — firmware, edge compute, device-cloud protocols (MQTT, CoAP), fleet management, OTA updates, and the data pipelines that ingest telemetry from devices at scale.
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