Overview

CAREER LEVEL: GCF 5 – Specialist

Responsibilities

• Define the user, workflow, intended use, baseline, acceptance criteria, accessibility needs, adoption path, operating owner, value hypothesis and measurable technical and business outcomes.
• Map rules, handoffs, exceptions, data dependencies and human decision points before selecting conventional software, automation, ML, GenAI, RAG, agents or a manual approach.
• Lead sprint and release planning, estimates, capacity, dependencies, MVP and phased roadmaps, explicit assumptions, total cost of ownership and support transition.
• Own production architecture across human-AI experience, frontend, API gateway, backend services, persistence, events, integrations, data/knowledge flows, AI capabilities, security zones and human review.
• Lead responsive frontend and backend delivery using modern JavaScript/TypeScript and Python, Node.js, Java or C#, with stable contracts, asynchronous workflows, retries, idempotency, compensation, rate limits and graceful degradation.
• Integrate model services, LLMs, RAG, search, agents, BI and deterministic automation with access-aware data, validation, representative evaluation, human control, feedback and traceable evidence.
• Establish risk-based testing, AI evaluation, CI/CD, policy gates, infrastructure as code, staged release, SLOs, telemetry, rollback, incident response, disaster recovery, capacity, cost, runbooks and support ownership.
• Coordinate governance and GxP controls, create reusable components and playbooks, lead root-cause analysis, measure adoption and value, mentor engineers and improve delivery practices.

Qualifications

• Bachelor’s/Master’s degree with 8 - 13 years of experience in Computer Science, IT or related field.
• End-to-end full-stack and human-AI engineering: User journeys, accessible frontend architecture, state, forms, secure sessions, streaming, review, correction, approval, uncertainty, feedback, telemetry and recoverable failure.
• Backend, API, data and distributed-systems architecture: Services, events, workflows, persistence, caches, queues, transactions, retries, idempotency, compensation, multi-tenancy, consistency, resilience, contracts, data models and enterprise integrations.
• AI-enabled applications and automation: Production integration of predictive ML, foundation models, embeddings, vector/graph retrieval, RAG, structured output, bounded agents, document/vision capabilities, BI and deterministic workflows.
• Testing, cloud, DevSecOps and reliability: Representative testing and AI evaluation, repository governance, architecture decisions, CI/CD, infrastructure as code, quality gates, staged release, SLOs, observability, incidents, rollback, disaster recovery and FinOps.
• Security, Responsible AI and regulated delivery: Identity, authorization, least privilege, tenant isolation, secrets, OWASP, supply-chain controls, privacy, threat modeling, prompt/tool safeguards, validation, auditability, accessibility and GxP evidence.
• Demonstrated end-to-end ownership of at least one production full-stack application, digital product, automation or AI-enabled system that delivered a measurable outcome.
• Strong hands-on proficiency in JavaScript or TypeScript, at least one backend language such as Python, Java, C# or Node.js, and SQL, with experience designing production services.
• Ability to lead application and integration architecture, delivery, release, operations and support while partnering with ML specialists for deeper model, RAG, agent and MLOps decisions.
• Advanced frontend and human-AI platforms: Experience with Next.js or comparable rendering patterns, design systems, micro-frontends where justified, multimodal or collaborative interfaces, advanced accessibility and safe agent approval experiences.
• Distributed and enterprise integration: Experience with Kafka or equivalent event platforms, durable workflows, outbox or saga patterns, stream processing, service mesh, API gateways and large-scale integration architecture.
• Advanced AI and automation engineering: Experience with vector or graph databases, graph or multimodal retrieval, model and agent gateways, MCP-style tool integration, durable agents, agent evaluation, process mining, UiPath or n8n.
• Cloud, platform and developer experience: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless services, infrastructure as code, OpenTelemetry, policy as code or internal developer platforms.
• Regulated systems and technical breadth: Experience delivering GxP-relevant, validated, pharmaceutical, healthcare or other high-impact systems and evaluating emerging technologies pragmatically.
• Product thinking and ability to connect architecture and delivery decisions to measurable user, workflow and business value.
• Strong technical leadership, mentoring and ownership while remaining hands-on.
• Excellent analytical judgment, troubleshooting and clear communication of evidence, limitations and trade-offs.
• Cross-functional leadership with product, business, UX, architecture, ML, platform and control functions.
• Pragmatism, resilience and continuous improvement through incidents, feedback, reuse and measured outcomes.