Interested in this AI/ML Engineer role at Allianz?
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About This Role
What you do:
The Senior Data Scientist (AI Engineer) is a member of the Data Science AI team within the Data Analytics Center of Excellence. You will take ownership of designing, building, and supporting production-grade AI systems using GenAI, Agentic AI and/or machine learning to solve challenging business problems across the Enterprise. You will report to the Data Science Senior Manager and will work with an AI delivery team consisting of a tech lead, product owner, and other data scientists/AI engineers.
- Design, build, and maintain production-grade generative AI and agentic AI systems (e.g., LLM-based applications) that are secure, scalable, and reliable enough for enterprise-wide deployment
- Productionize models and AI systems, working within established CI/CD pipelines and DevOps practices (Github Actions) to support deployment, versioning, release management, and monitoring/alerting for performance, drift/quality, and reliability at scale
- Build and maintain Databricks pipelines for data processing and pipeline support for GenAI, agentic, and machine learning workloads at scale
- Communicate technical designs, trade-offs, and system behavior to both technical and non-technical stakeholders to inform project decisions
- Scope and reframe ambiguous business problems into concrete technical plans for GenAI, agentic, and machine learning solutions
- Provide thought leadership on new methodologies in AI and engineering to elevate standards across the department
- Mentor and support the technical development of junior team members
- Utilization of artificial intelligence tools and resources (e.g. generative AI)
What you bring:
- Minimum 4+ years of experience required or 3+ years with a graduate degree
- Hands-on experience building generative AI and agentic solutions in Python, including RAG solutions, agent frameworks, and LLM orchestration (not just no-code/low-code tools)
- Demonstrated experience working with cloud computing and big data platforms (Azure and Databricks preferred) sufficient to deploy, operate, and release AI/ML systems in production
- Strong proficiency in writing clean, production-ready code in Python
- Previous experience leading data-driven projects from concept to execution, translating complex insights into actionable strategies and confidently presenting findings to stakeholders and senior leadership
- Experience with version control using Git / Github
- Experience evaluating, productionizing, and monitoring traditional AI/ML models using Databricks (or equivalent), including using jobs/workflows, MLflow for experiment tracking and model management, data pipelines, and Delta Lake tables desired
- Experience building and integrating with RESTful APIs, connecting AI/ML systems to enterprise data sources and existing business applications, with an understanding of overall systems architecture and how these components fit together desired
- Understanding of machine learning algorithms and core statistical concepts (e.g., hypothesis testing) desired
- Experience with Claude Code or similar AI coding agent tools for building and prototyping AI solutions desired
- Kubernetes or containers experience desired
- You must be legally authorized to work in the U.S. without requiring immigration sponsorship now or in the future. This includes holders of H-1B, H4, L-1, L-2, TN, OPT, CPT, and other nonimmigrant visas.
Job posting range $110,001 - $164,000
Note the job posting range is exclusive of company benefits or other compensation. This role is also eligible for an annual incentive program. If you are hired, your final base salary will be determined based on factors such as geographic location, experience, skills, competencies, and/or education. In addition to those factors, we will also consider internal equity of our current employees. Please keep in mind that the range provided is the full base salary range for the role. Hiring at or near the maximum of the range would not be typical to allow for future and continued salary growth.
What we offer:
At Allianz Life, we’re proud to provide a benefits package that supports the True Balance of our employees and their families. We offer: a choice of comprehensive medical, dental and vision plan options, flexible spending and health savings accounts, tuition reimbursement, student loan retirement program, generous annual paid leave, an outstanding 401(k) company match, and life insurance. Plus, our employees enjoy an award-winning campus with an array of discounted amenities, including an onsite health center, child development center, fitness facility, convenience store and two cafeterias. Our employees and leaders take an active role in shaping our culture through our Employee Resource Groups, whose activities and advocacy contribute to an environment that is fair, caring, and inclusive. Our generous benefits, campus amenities and inclusive, engaged culture are among the many reasons we’re routinely recognized as a top workplace employer. For additional information regarding What It’s Like To Work Here | Allianz Life.
107119 | Data AI | Professional | Non-Executive | Allianz US Life | Full-Time | Permanent
Allianz Group is one of the most trusted insurance and asset management companies in the world. Caring for our employees, their ambitions, dreams and challenges, is what makes us a unique employer. Together we can build an environment where everyone feels empowered and has the confidence to explore, to grow and to shape a better future for our customers and the world around us. At Allianz, we stand for unity: we believe that a united world is a more prosperous world, and we are dedicated to consistently advocating for equal opportunities for all. And the foundation for this is our inclusive workplace, where people and performance both matter, and nurtures a culture grounded in integrity, fairness, inclusion and trust. We therefore welcome applications regardless of race, ethnicity or cultural background, age, gender, nationality, religion, social class, disability or sexual orientation, or any other characteristics protected under applicable local laws and regulations.
Join us. Let’s care for tomorrow. #LI-BK1
Salary Context
This $110K-$164K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Allianz, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $110K to $164K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Allianz AI Hiring
Allianz has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US. Compensation range: $164K - $164K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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