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About This Role
BlueFlag is hiring Senior Databricks Engineers to help Department of Veterans Affairs teams move their data work to a premier data and artificial intelligence (AI) platform built on Databricks in Azure Commercial. The role is hands-on and customer-facing, much like professional services. You will work alongside VA teams to understand their on-premises workloads, rebuild them on Databricks, and teach them to use the platform with confidence.
We're hiring for two focus areas. The ML focus helps teams build, deploy, and monitor machine learning models. The Analytics focus helps teams move ETL pipelines and reporting to Databricks. Both need the same foundation: a data engineer who has built production pipelines and enjoys helping customers succeed. You'll join a friendly, collaborative team doing work that makes a real difference for Veterans and their families.
What You'll Do
- Work directly with VA teams to scope use cases, plan migrations, and deliver working solutions on Databricks.
- Assess on-premises workloads (SQL Server, SSIS packages, stored procedures, scheduled jobs) and adapt them to Databricks patterns in Azure Commercial.
- Build and tune pipelines with PySpark, Spark SQL, Delta Lake, Lakeflow, and Azure Data Factory.
- Use Unity Catalog for access, lineage, and sharing, so customer data stays governed as it moves.
- Teach customers through workshops, pairing, and clear documentation so they can own their workloads.
- Package reusable templates and code (Git, CI/CD, Databricks Asset Bundles) that speed up the next migration.
AI / ML focus
- Build end-to-end ML workflows: feature engineering, training, evaluation, MLflow tracking, the Unity Catalog model registry, and model serving.
- Help data scientists move models from local notebooks and on-premises servers into governed, monitored production.
- Support generative AI use cases, such as retrieval-augmented generation (RAG) and agents, alongside predictive models.
Analytics focus
- Convert SSIS and stored procedure ETL into Databricks pipelines, with reconciliation that proves the results match.
- Replace on-premises reporting (SSRS reports, SSAS cubes) with Databricks SQL, AI/BI dashboards, and Power BI.
- Publish reporting data from Databricks SQL warehouses, including semantic models and query performance tuning.
Why Join BlueFlag
At BlueFlag, we're passionate about leveraging cutting-edge technology to make a real difference. You'll be at the forefront of cloud innovation, working on projects that directly impact people's lives. We offer a high-growth, entrepreneurial environment that values fresh ideas and authentic teamwork.
If you're ready to take your data engineering career to new heights and contribute to meaningful projects that push the boundaries of technology, we want to hear from you. Join BlueFlag and be part of a team that's shaping the future of cloud solutions!
Requirements
- 7+ years in data engineering, including 2+ years building production workloads on Databricks
- Strong proficiency in Python, SQL, PySpark, and Spark SQL
- Hands-on experience with Delta Lake, lakehouse (medallion) design, and Spark performance tuning
- Hands-on Unity Catalog experience: catalogs, grants, lineage, and row- and column-level security
- Experience migrating on-premises workloads (e.g., SQL Server, SSIS, stored procedures) to the cloud
- Experience with Azure services, including ADLS Gen2, Azure Data Factory, Entra ID, and Key Vault
- Experience with Git, CI/CD, and infrastructure as code (e.g., Databricks Asset Bundles, Terraform)
- Customer-facing consulting or professional services experience, including requirements sessions, workshops, and training
- Clear written and verbal communication with technical and non-technical audiences
- Bachelor's degree in computer science, information systems, or a related field.
- US Citizen: Must be a citizen of the United States
- Security Clearance: Must be able to obtain a public trust clearance. Must be eligible to work in the United States.
AI / ML focus
- Experience training, evaluating, and deploying models with MLflow and common frameworks (e.g., scikit-learn, XGBoost, PyTorch)
Analytics focus
- Experience with Databricks SQL and Power BI data modeling (star schemas, semantic models, DAX)
Desired
- Databricks certifications (Data Engineer Professional; Machine Learning Professional or Data Analyst Associate by focus)
- Experience with healthcare data (HL7, FHIR, OMOP) and HIPAA requirements for PHI
- Experience in Azure Government or other FedRAMP High environments
- Experience upgrading Hive metastore workloads to Unity Catalog (e.g., UCX)
- AI / ML focus: Mosaic AI Vector Search, Agent Framework, or model monitoring
- Analytics focus: Microsoft Fabric, Pyramid Analytics, or Genie
Benefits
- Competitive salary
- Generous annual leave and paid holidays
- Comprehensive group health and dental plans
- 401(k) with company match
- Life insurance and AD&D coverage
- Ongoing training and professional development opportunities
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 BlueFlag LLP, 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.
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.
BlueFlag LLP AI Hiring
BlueFlag LLP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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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