AI Solutions Engineer - Internal Delivery at Allianz Global Corporate & Specialty SE

Munich, Bavaria, Germany -

Full Time

Start Date

Immediate

Expiry Date

24 Jan, 26

Salary

0.0

Posted On

26 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Solutions Engineering, Data Integration, Machine Learning, Cloud Computing, Python, TypeScript, SQL, Predictive ML, GenAI, APIs, Incident Management, Stakeholder Engagement, GDPR Compliance, Continuous Improvement, Monitoring, Deployment

Industry

Financial Services

Description

Key responsibilities As an AI Solutions Engineer within Advanced Analytics (DA3), Chief Data Office Area - Allianz Partners, you report to the Head of Advanced Analytics. You embed with internal business units and product teams to turn AI opportunities into production outcomes. You scope on real workflows, prototype quickly, and ship full-stack solutions that integrate with enterprise systems, then harden them for reliability, scale, and compliance. Working closely with the Lead for AI Architecture & Implementation, you align to reference architectures, patterns, and guardrails to ensure robust, secure, and cost-effective delivery across a B2B2C context. As an AI Solutions Engineer, you are in charge of the following: Embed & own delivery: Translate internal objectives into scoped workstreams, delivery plans, and acceptance criteria; hold the bar on KPIs/SLAs, cost, and timelines from first prototype to stable operations. Shape solution design: Select fit-for-purpose approaches (predictive ML, GenAI, agents, decisioning, automation), data sources, and integration patterns; define NFRs (security, privacy, performance, reliability, cost). Data integration & discovery: Inventory and profile data sources and target/output systems; assess and prototype source connections (APIs, event streams, DBs, files) and output integrations (apps, services, analytics/BI, operational systems). Build hands-on: Develop production features (APIs, orchestration, evaluators, prompts, simple UIs) and integrate with our internal platform (data/storage, compute, identity, networking, monitoring) to ship usable software—not just POCs. Launch & scale: Run pilots/A-B tests and phased rollouts with internal teams; drive change management, training, and enablement to achieve adoption and measurable uplift in business KPIs. Operate & improve: Establish monitoring, evaluations/guardrails, incident handling, and continuous improvement for quality, drift, safety, and cost; maintain runbooks, service/model cards, and playbooks. Governance by design: Embed GDPR, privacy-by-design, Responsible AI controls, and EU AI Act readiness with Risk, Legal, and Compliance from discovery through operations. Stakeholder engagement (internal-first): Communicate progress, risks, and trade-offs in business terms to product owners and leaders; coordinate with internal platform and data teams; remove delivery blockers. Troubleshoot end-to-end: Triage data, model, and integration issues across environments; drive rapid root-cause and remediation. What you bring 2 to 4 years delivering AI/analytics solutions in enterprise settings with end-to-end involvement from scoping to steady-state ops. Education: Bachelor’s/Master’s in CS, Data/AI, Statistics, or related—or equivalent practical experience delivering AI solutions. Engineering-first mindset: Proficiency writing production code (e.g., Python/TypeScript/SQL); comfortable standing up integrations, workflows, and lightweight UIs to meet internal user needs quickly. AI solution patterns: Working knowledge across predictive ML, GenAI/LLMs, and AI agents; experience with evaluation, guardrails, and reliability in production. Cloud & platform: Practical experience with Azure for enterprise deployment; bonus for containers/Kubernetes, CI/CD, and observability. ML/AI operations: Experimentation, deployment, monitoring, incident management, lifecycle controls, and cost/performance optimization. Internal stakeholder fluency: Ability to translate business needs into delivery plans; communicate complex topics clearly to non-technical stakeholders; thrive in ambiguous, cross-functional environments. Governance: Working knowledge of GDPR, EU/AI Act, Responsible AI, and control frameworks; experience partnering with Risk/Compliance. Tools: Effective use of AI-assisted dev tools (e.g., GitHub Copilot) to accelerate build and delivery. Languages: English (fluent). What we offer Our employees play an integral part in our success as a business. We appreciate that each of our employees are unique and have unique needs, ambitions and we enjoy being a part of their journey. We are there to empower and encourage you with your personal and professional development ensuring that you take control by offering a large variety of courses and targeted development programs. All that in a global environment where international mobility and career progression are encouraged. Caring for your health and wellbeing is key priority for us. This is why we build Work Well programs to providing you with peace of mind and give the flexibility in planning and arranging for a better work-life balance. 80588 | Data & AI | Management | Allianz Partners | Full-Time | Permanent

Responsibilities

The AI Solutions Engineer will embed with internal business units to turn AI opportunities into production outcomes, developing full-stack solutions that integrate with enterprise systems. Responsibilities include shaping solution design, launching and scaling pilots, and ensuring compliance with governance standards.