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About This Role
USA · Full\-time · Intermediate
#### About The Position
Data \& AI Senior Technical Delivery Manager
Matrix's Data \& AI practice builds and runs modern data platforms and AI solutions for enterprise clients across financial services, CPG, retail, and construction. We are looking for a Senior Technical Delivery Manager to own end\-to\-end delivery on our most strategic accounts — the person the client trusts to make the program land, and the person our engineers trust to clear the road ahead of them.
This is a hands\-on leadership role. You will not be writing production code day to day, but you will be technical enough to challenge an architecture decision, read a Databricks job failure and know what it means, and hold your own in a design review with a client's Chief Data Officer.
What You'll Do
Own delivery
- Lead multi\-workstream Data \& AI programs from kickoff through steady\-state, with accountability for scope, schedule, margin, quality, and client outcomes.
- Build and maintain delivery plans, RAID logs, sprint cadences, and governance rhythms that hold up under executive scrutiny.
- Run status reporting and steering committees that surface risk early rather than explaining it late.
- Manage change control: identify scope drift, quantify impact, and negotiate change orders without damaging the relationship.
Lead teams
- Lead blended onshore/offshore teams of 8–25 engineers, architects, analysts, and consultants across multiple concurrent workstreams.
- Set delivery standards and hold the team to them — code review discipline, documentation, environment hygiene, DevOps practices.
- Coach and develop team members; own staffing plans, ramp\-up, and performance feedback in partnership with practice leadership.
- Resolve technical and interpersonal blockers quickly, escalating only what genuinely needs escalating.
Own the client relationship
- Serve as the primary day\-to\-day point of contact for client program sponsors, data leaders, and business stakeholders.
- Translate between business intent and technical reality in both directions — and say the hard thing when a timeline or an approach won't work.
- Run executive\-level communication: QBRs, KPI reviews, escalation handling, and recovery plans when a program is off track.
- Protect client satisfaction as a measurable outcome, not a feeling.
Drive technical quality
- Provide oversight, with practice architects, on solution design across Snowflake, Databricks, and the surrounding modern data stack.
- Ensure delivery follows sound patterns for data modeling, pipeline orchestration, governance, security, cost management, and production support.
- Bring judgment on build vs. buy, platform trade\-offs, and technical debt — and make sure those decisions are documented and defensible.
- Oversee delivery of AI/ML and GenAI workloads, including model lifecycle, evaluation, and responsible\-AI considerations where applicable.
Grow the account
- Identify and shape follow\-on opportunities within existing accounts; contribute to SOWs, estimates, and staffing models.
- Support pre\-sales as the delivery voice in pursuits — solution shaping, effort estimation, risk assessment, and client presentations.
- Contribute to practice assets: delivery playbooks, accelerators, reusable frameworks, and lessons\-learned.
What You Bring
Required
- 10\+ years in technology delivery, with at least 5 years managing complex data or analytics programs.
- Consulting or professional services background — you understand utilization, margin, SOWs, change orders, and the difference between a client and an employer.
- Hands\-on working knowledge of Snowflake and/or Databricks in production environments (required). You should be able to speak credibly about warehouse vs. lakehouse architecture, Delta/Iceberg, Unity Catalog or Snowflake governance, workload optimization, and platform cost drivers.
- Familiarity with the surrounding modern stack: ELT and orchestration (dbt, Airflow, ADF, Fivetran), cloud platforms (Azure, AWS, or GCP), BI and semantic layers (Power BI, Tableau), and CI/CD for data.
- Demonstrated experience leading distributed onshore/offshore delivery teams.
- Track record of direct executive\-level client engagement, including at least one program you personally recovered or turned around.
- Strong command of Agile and hybrid delivery methods, and the judgment to know which one a given client actually needs.
- Excellent written and verbal communication; comfortable presenting to a room of senior stakeholders.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
Preferred
- Experience in one or more of: financial services, CPG, retail, or construction/AEC.
- Exposure to Dataiku, MLOps tooling, or GenAI and agentic solution delivery.
- Prior background as a data engineer, BI developer, or solution architect before moving into delivery leadership.
- Certifications: Databricks, Snowflake (SnowPro), cloud platform (Azure/AWS/GCP), PMP, or SAFe.
- Experience with data governance, data quality, or regulatory\-driven programs (privacy, AML, model risk).
- Ability to travel to client sites as needed (typically \[X]%).
What Success Looks Like
First 90 days — You know your accounts, your teams, and your risks. Delivery plans are current, governance cadence is running, and clients know who you are.
First 6 months — Programs are landing on schedule and on margin. Escalations are down. At least one follow\-on opportunity has been shaped from within an existing account.
First 12 months — Your accounts are reference\-able. Your team is stronger than when you inherited it, and something you built — a playbook, an accelerator, a delivery standard — is being reused across the practice.
Why Join Us?
You’ll work with a talented global team, contribute to meaningful technical initiatives, and help shape a high‑performing DevOps culture.
Matrix is a global, dynamic, and fast‑growing technology services and consulting company with over 17,000 employees worldwide. Founded in 2001, Matrix has grown through significant organic expansion and strategic acquisitions, executing some of the largest and most impactful technology projects in the market.
We specialize in the design, development, and implementation of advanced technologies, software solutions, and digital products. Our services include infrastructure and consulting, IT outsourcing and offshore delivery, training and assimilation programs, and acting as a trusted implementation and delivery partner for the world’s leading software vendors.
With deep expertise across both the private and public sectors—spanning Finance, Telecom, Healthcare, Hi‑Tech, Education, Defense, and Security—Matrix serves a strong customer base in Israel alongside a continuously expanding global client portfolio.
At Matrix, our people are at the heart of our success. We are a community of talented, creative, and dedicated professionals who are passionate about innovation and excellence. We actively attract, develop, and retain top talent, recognizing that every employee’s contribution is critical to our continued growth and future success.
Matrix’s success is built on a challenging and engaging work environment, competitive compensation and benefits, and meaningful career development opportunities. We foster a diverse, inclusive culture that encourages collaboration, continuous learning, and growth—together.
Join the winning team. Be challenged, grow your career, and enjoy the journey in a highly respected global organization.
To learn more, visit: www.matrix\-ifs.com
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Matrix International Financial Services, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Matrix International Financial Services AI Hiring
Matrix International Financial Services has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Miami, FL, US, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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