AI Data & Integration Engineer

About the Role

A technology company running an enterprise AI transformation program is looking for a hands-on AI Data & Integration Engineer. The program spans multiple workstreams — developer productivity, operational support, and business process optimization — and needs someone who can own the technical plumbing that makes it all run.

This is fundamentally a builder’s role. You will install and configure a workflow orchestration platform from scratch, build the data pipelines that feed AI systems, and make sure the different parts of a complex multi-vendor program actually connect and work together. A significant part of the role also involves being the technical go-to person for internal teams adopting new AI tools.

The right candidate is someone who gets things done, communicates well with engineers and non-engineers alike, and is energized rather than frustrated by messy, fast-moving environments where the map is still being drawn.

• Plan and execute a full enterprise workflow orchestration platform installation (KNIME): infrastructure sizing, OS and Java prerequisites, network and firewall configuration, and license activation.
• Configure the platform for team use: user authentication (LDAP/Active Directory), workspace management, executor setup, and connectivity to organizational data sources.
• Establish validated connections to cloud storage, shared file systems, REST APIs, database endpoints, and third-party SaaS platforms.
• Set up scheduled job execution, pipeline health monitoring, and automated alerting via email or messaging platforms.
• Write installation documentation and operational runbooks so the platform can be maintained and extended by internal staff after handoff.

• Design and build orchestration workflows for: structured content ingestion and validation, entity matching and master data management, knowledge base publishing and versioning, and operational KPI reporting.
• Define and enforce data ingestion standards: package formats, metadata schemas, naming conventions, completeness checks, and checksums.
• Manage pipeline versioning and controlled promotion through development, staging, and production environments, including rollback capability.
• Implement automated regression and sanity checks as part of the release process to catch data quality regressions early.

• Configure AI-assisted coding tools for use within the developer IDE environment, including authentication, policy enforcement, and integration with source control and CI/CD systems.
• Translate internal policy requirements into concrete tool configurations: access controls, restricted paths, output templates, and pipeline gating rules.
• Support engineers during onboarding to new AI tooling — hands-on troubleshooting of IDE setup, authentication issues, and pipeline connectivity.
• Document tool behavior, identify configuration gaps, and produce structured evaluation notes for program review.

• Serve as the technical integration point between external implementation partners and internal engineering teams, ensuring APIs, data formats, and handoff contracts are clearly specified and tested.
• Own the integration test plan for cross-system interfaces: data ingestion flows, event-driven triggers, artifact exchanges, and end-to-end validation across vendor boundaries.
• Proactively surface and escalate integration blockers — access delays, API gaps, schema mismatches — with clear proposed mitigations.
• Maintain living interface specification documentation as integrations evolve through the program lifecycle.

• Be the day-to-day technical point of contact for engineering, QA, and operations staff adopting new AI and data tooling.
• Run practical onboarding sessions for pilot user groups — working directly in the tools, not delivering slide presentations.
• Triage incoming technical feedback: classify issues accurately (bug vs. configuration vs. user error vs. feature request) and route each to the right owner with enough context to act.
• Represent the implementation perspective in program meetings — give honest, current status rather than optimistic summaries.

• Hands-on experience with KNIME: building workflows, configuring connectors, scheduling jobs, and setting up either KNIME Server or KNIME Analytics Platform in an enterprise context.
• Solid REST API and systems integration experience: endpoint testing, webhook configuration, and working with structured data formats (JSON, CSV, XML).
• Experience with a cloud-based DevOps or source control platform (Azure DevOps, GitHub, or equivalent): repositories, pipelines, and artifact management.
• Practical experience configuring developer tools or IDE extensions, including authentication and access policy enforcement.
• Clear written and verbal communication — comfortable writing technical documentation and explaining technical topics to non-technical stakeholders.
• Self-directed and reliable in environments with multiple workstreams and external partners.

• Python or scripting proficiency sufficient to write data validation logic, debug pipelines, and automate repetitive tasks.
• Working familiarity with AI/ML application concepts: retrieval-augmented generation (RAG), vector databases, embedding pipelines, and LLM integration patterns — at an implementation and integration level.
• Prior experience in multi-vendor program environments where integration coordination was a core part of the role.
• Cloud platform experience (Azure, AWS, or GCP): storage, compute, networking basics.

• Exposure to ERP systems and extract-based integration patterns.
• Experience with knowledge base, document management, or content operations pipelines.
• Background in software QA, testing infrastructure, or developer productivity tooling.

You will be joining an enterprise AI program at its implementation phase where the designs are defined and the real work of making them run begins. 

You will be one of a small number of people responsible for making the shared technical infrastructure that underpins all business domains work reliably. That means your work has broad impact and high visibility, but also requires someone who can prioritize, make pragmatic decisions, and keep multiple balls in the air without dropping them.

The team values directness, technical substance, and follow-through over process and ceremony.