Senior Consultant - Data & AI Solutions
Allianz
What you do
- Lead end‑to‑end delivery and own solution architecture for cloud‑based AI on Azure/AWS using Python and SQL—focusing on GenAI applications (e.g., RAG and agentic workflows) that deliver measurable business outcomes.
- Translate business goals into technical roadmaps, designs, and success metrics; define scope, milestones, and acceptance criteria; manage timelines and risks to ensure on‑time, high‑quality delivery.
- Build, harden, and scale services with solid engineering practices (CI/CD, containerization, Infrastructure as Code with Terraform), robust data pipelines on Databricks Lakehouse architecture (Delta, Unity Catalog), and secure integrations (APIs/events); ensure data quality and privacy by design.
- Establish and enforce responsible AI guardrails (safety, privacy, evaluation); lead compliance with Allianz security and governance standards.
- Instrument solutions for telemetry and analytics; optimize performance, reliability, and cost; track and report benefits against KPIs and ROI.
- Manage stakeholders across Allianz entities: facilitate workshops, communicate trade‑offs, produce executive‑ready presentations/demos, and align cross‑functional teams.
- Mentor junior engineers; drive coding standards, documentation quality, PR review rigor, and reusable components/playbooks (templated repos, CI/CD via GitHub Actions) to enable scale.
- Drive innovation and vendor/tool evaluation: identify new AI opportunities, run experiments, deliver prototypes and business cases, and support proposals/pre‑sales when needed.
What you bring
- 3+ years of hands‑on experience in AI/ML application engineering or technical consulting, with a track record of shipping production solutions.
- Advanced Python and strong SQL; demonstrated experience building LLM applications (RAG architectures, Agentic Workflows) and using orchestration frameworks (e.g., LangChain, LlamaIndex).
- Cloud proficiency with Azure or AWS AI services (e.g., Azure OpenAI, Cognitive Search; AWS Bedrock), IAM/security hygiene; containers and Kubernetes (AKS/EKS), Infrastructure-as-Code (Terraform), and CI/CD (GitHub Actions) for deployments.
- MLOps/LLMOps practices: experiment tracking, prompt/model versioning, offline/online evaluation, monitoring/logging, and incident response.
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