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About This Role
We're hiring an AI Solutions Architect who operates at the intersection of applied AI, enterprise data engineering, and cloud\-native .NET development. This isn't an advisory role — you'll own architecture decisions end to end, from Databricks lakehouse design through to production LLM and agentic AI systems built on .NET and Azure. You'll work closely with executive stakeholders, engineering squads, and data science teams to shape how intelligent systems are built and scaled across the organization.
What you'll do
- Define and own the enterprise AI architecture strategy — spanning data pipelines, model lifecycle, and intelligent application delivery across Azure.
- Design and govern production Databricks lakehouse platforms: Medallion architecture (bronze/silver/gold), Delta Lake, Unity Catalog governance, and MLflow\-based model lifecycle management.
- Architect and deliver end\-to\-end AI/ML solutions on the .NET ecosystem, integrating Azure OpenAI, Semantic Kernel, and Azure AI Studio into scalable enterprise applications.
- Build feature engineering pipelines on Databricks that feed production ML models and LLM\-grounded retrieval systems.
- Lead design of RAG pipelines, vector stores, embedding strategies, and agentic workflows — bridging the data platform to the application layer.
- Lead technical design sessions, set architecture standards, and drive AI governance — reliability, security, observability, and responsible AI practices.
- Translate complex business problems into AI architectures; communicate trade\-offs and roadmap decisions to executive and non\-technical stakeholders.
- Mentor senior engineers across squads and provide hands\-on technical leadership through delivery — not just design.
- Stay current on emerging LLM capabilities and proactively surface adoption opportunities aligned to business outcomes.
What you bring
8–10 years of hands\-on software engineering and architecture experience — with genuine ownership of production systems, not advisory or oversight roles.
At least 3 years in a solutions or enterprise architect role, with a track record of driving technology decisions at the director or VP level.
Strong command of C\# / .NET (Core / .NET 6/7/8\) and cloud\-native Azure patterns — microservices, event\-driven design, API\-first architecture, AKS deployments.
Production\-grade Databricks experience: Delta Lake, PySpark/SQL, Databricks Workflows, Medallion architecture, Unity Catalog, and MLflow on Databricks.
Hands\-on experience designing and deploying AI/ML systems in production — LLMs, RAG, embeddings, fine\-tuning, or agentic architectures.
Proficiency with Azure OpenAI Service, Semantic Kernel, Azure AI Studio, and vector databases (Azure AI Search, Pinecone, or Qdrant).
Deep familiarity with distributed systems, event\-driven design (Service Bus, Kafka, Event Grid), and enterprise API patterns (REST, gRPC).
Excellent communication skills — able to write ADRs, run design sessions, and present architecture trade\-offs to executives and engineers alike.
Nice to have
- Experience with Databricks Model Serving endpoints or Feature Store integration with real\-time inference pipelines.
- Background in regulated industries — fintech, healthcare, insurance, or legal — with compliance framework experience (SOC 2, HIPAA, PCI\-DSS).
- MLOps tooling beyond MLflow: Azure ML pipelines, Kubeflow, or Databricks AutoML.
- Contributions to open\-source AI, data engineering, or .NET ecosystem projects.
- Experience with multi\-cloud architectures spanning Azure and AWS or GCP.
Tech stack
Backend / App
.NET 8 / C\#, ASP.NET Core, REST, gRPC, Azure Service Bus, Event Grid
AI / LLM
Azure OpenAI, Semantic Kernel, Azure AI Studio, LangChain, RAG, Agentic AI
Data platform
Databricks, Delta Lake, MLflow, Unity Catalog, Databricks Workflows, PySpark
Cloud \& infra
Azure Kubernetes Service, Azure Data Factory, Azure Monitor, Terraform, GitHub Actions
Vector \& search
Azure AI Search, Pinecone, Qdrant, FAISS, pgvector
Databases
SQL Server, Azure Cosmos DB, PostgreSQL, MongoDB
Why this role stands out
Most AI architect roles live in one world — either the data platform or the application layer. This role owns both. You'll design the Databricks pipelines that govern and prepare data, and architect the .NET AI systems that consume and act on it. If you're energized by closing the gap between enterprise data engineering and production AI delivery — and want to do it in one of the most architecturally rich tech markets in the US — this role was built for you.
Hard requirements — please read before applying
Location: This role is based in Chicago, IL. Candidates must be able to work on\-site or in a hybrid capacity in the Chicago area. Remote\-only applicants will not be considered.
Work Mode : Contract
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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