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About This Role
About the role
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We're looking for a seasoned AI Applied Architect with deep .NET expertise and hands\-on Databricks experience to lead the design and delivery of enterprise\-grade AI and data intelligence systems. You'll sit at the intersection of software architecture, applied AI, and modern data engineering — shaping how we build, scale, and govern intelligent applications across our product portfolio. This is a high\-impact role with visibility at the executive level and meaningful influence over our long\-term technology roadmap.
What you'll do
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- Architect and deliver end\-to\-end AI/ML solutions on the .NET ecosystem, including integration with Azure AI, OpenAI, and Semantic Kernel.
- Design and own enterprise\-scale Databricks lakehouse architectures — including Medallion (bronze/silver/gold) pipelines, Delta Lake, Unity Catalog governance, and MLflow\-based model lifecycle management.
- Lead technical design sessions, define architecture standards, and drive decision\-making for AI\-powered product features.
- Collaborate with product managers, data scientists, and engineering teams to translate business requirements into scalable AI and data architectures.
- Evaluate and recommend frameworks, tools, and cloud services for AI workloads — model serving, RAG pipelines, vector stores, agents, and feature engineering on Databricks.
- Build and govern feature engineering pipelines on Databricks, feeding production ML models and LLM\-grounded retrieval systems.
- Establish and enforce best practices for AI system reliability, security, observability, and responsible AI governance.
- Mentor senior engineers and provide technical leadership across multiple squads.
- Stay current on emerging AI/LLM capabilities and proactively identify opportunities for adoption.
What you bring
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- 8\+ years of software engineering experience, with at least 3 years in a solutions or enterprise architect role.
- Strong command of C\# / .NET (Core / .NET 6/7/8\) and cloud\-native patterns on Azure.
- Hands\-on experience designing and deploying AI/ML systems in production — LLMs, RAG, embeddings, fine\-tuning, or agentic architectures.
- Proficiency with Azure OpenAI Service, Azure AI Studio, Semantic Kernel, and/or LangChain equivalents in .NET.
- Production\-grade Databricks experience: Delta Lake, PySpark/SQL, Databricks Workflows, Medallion architecture, Unity Catalog, and MLflow on Databricks.
- Deep familiarity with microservices, event\-driven design, API design, and distributed systems.
- Proven track record leading cross\-functional teams and driving large\-scale technology initiatives.
- Excellent communication skills — able to translate complex technical concepts for executive and non\-technical audiences.
Nice to have
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- Experience with MLOps tooling beyond MLflow: Azure ML, Kubeflow, or Databricks Model Serving endpoints.
- Familiarity with vector databases (Pinecone, Qdrant, Azure AI Search).
- Background in regulated industries (fintech, healthcare, legal).
- Experience integrating Databricks Feature Store with real\-time inference pipelines.
- Contributions to open\-source AI/ML or data engineering projects.
Tech stack
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Category Technologies
Backend .NET 8 / C\#, ASP.NET Core, gRPC, REST APIs
AI / LLM Azure OpenAI, Semantic Kernel, Azure AI Studio
Data Platform Databricks (Delta Lake, MLflow, Unity Catalog, Workflows)
Cloud \& Infra Azure Kubernetes Service, Azure Data Factory, Azure Service Bus
Vector \& Search Azure AI Search, Pinecone, Qdrant, FAISS
Databases SQL Server, Azure Cosmos DB, PostgreSQL
DevOps GitHub Actions CI/CD, Docker, Kubernetes, Terraform
Observability Azure Monitor, Prometheus, Grafana, MLflow tracking
Why this role is different
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Most AI architect roles live either in the data platform world or the application/LLM world. This role owns both — you'll design the Databricks pipelines that prepare, govern, and serve data, and you'll architect the .NET AI systems that consume it. If you're energized by closing the gap between data engineering and applied AI delivery, this is built for you.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Symhas, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Symhas AI Hiring
Symhas has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chicago, IL, US, Pleasanton, CA, US.
Location Context
AI roles in Chicago pay a median of $202,000 across 283 tracked positions.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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