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About This Role
Head of AI Solutions Delivery
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About Andela
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Andela is transforming from a talent marketplace into an AI services leader, building a differentiated position at the intersection of elite technical talent and cutting\-edge AI solutions. We're growing our revenue exponentially through a blended model of optimized staffing, solutions delivery, and platform innovation. Our AI Solutions business is a strategic growth area with ambitious scaling plans and a mandate to redefine how enterprises build and deploy production AI systems.
The Opportunity
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We're seeking a Head of AI Solutions Delivery to build and lead the operational backbone of our rapidly scaling AI Solutions (outcome\-oriented) business. This is a pivotal role for someone who thrives at the intersection of rigorous process discipline and entrepreneurial agility; someone who can establish world\-class delivery standards while simultaneously adapting to the dynamic realities of a net\-new, high\-growth business.
You’ll be responsible for ensuring every customer engagement delivers exceptional outcomes: high CSAT, healthy margins, and demonstrable quality. You’ll do this through intelligent systems, scalable processes, and a culture of continuous improvement. The role requires someone who can be the “adult in the room” and the innovative voice pushing boundaries at the same time.
How This Role Fits
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The Head of AI Solutions Delivery is a peer to the Head of AI Solutions, who leads our AI Solutions Managers and AI Solutions Architects (our Forward\-Deployed Engineering function). Together, these two functions form the operating backbone of AI Solutions: AI Solutions builds the technical solution and embeds with the client. Delivery builds the program management muscle, methodology, and operational systems that make those engagements predictable, profitable, and scalable.
This role inherits Andela’s existing AI Solutions delivery team and is responsible for scaling it with a new generation of program managers who combine traditional PM rigor with technical and industry fluency. The expectation is straightforward: program managers in this function understand the AI technology they ship and the industries their clients operate in. Generic PM skills are table stakes, not the differentiator.
Exceptional Leadership:
As an Andelan, you’ll serve as a role model for the rest of the company. Think about the feedback your peers typically give you. If it usually sounds like the below, we want to hear from you.
- Low ego, low drama: You share credit, take blame. You like being wrong because it means someone else had an even better idea.
- One team mentality: You break silos across teams. You put the company and mission first above your team alone.
- Great listener, hungry for feedback: You’re always seeking to improve – our product, our business, yourself. You solicit diverse opinions and deeply listen.
- Owner, not renter: You see a problem, you fix it or find someone who will. The buck stops with you.
- Team\-player: You roll up your sleeves and get scrappy. You do this by proactively collaborating with your team while actively engaging in important details that matter.
- Business problem solver: You’re not just a functional expert; you consistently get praise for approaching your function through the lens of solving business problems.
What You'll Do
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Build Delivery Excellence Infrastructure
- Design and implement delivery frameworks, methodologies, and governance structures that scale AI Solutions revenue by several orders of magnitude over the next 24 months
- Establish clear SOPs, AI\-specific quality gates (eval frameworks, model performance thresholds, hallucination controls), and risk management protocols while maintaining flexibility for client\-specific needs
- Create metrics, dashboards, and reporting systems that provide real\-time visibility into delivery health, margin performance, and customer satisfaction
- Co\-develop delivery playbooks for common AI Solution patterns (agentic systems, RAG implementations, eval pipelines, model fine\-tuning engagements, multi\-agent orchestration, and industry\-specific AI use cases) that accelerate delivery while maintaining quality
Drive Operational Performance
- Own CSAT, margin, and quality metrics across all AI Solutions and custom development engagements
- Implement early warning systems to identify at\-risk projects and orchestrate rapid intervention
- Conduct regular business reviews with delivery teams to ensure adherence to financial targets and scope management
- Establish post\-mortem and lessons\-learned processes that systematically improve delivery capabilities
Scale Through Process Innovation
- Partner with AI Solutions, Sales, and Customer Success leadership to define outcome\-based delivery models and fixed\-fee engagement structures, and to ensure tight handoffs between Solutions Architects (FDEs) and delivery program managers on every engagement
- Build resource management and capacity planning systems that optimize utilization while preventing burnout
- Create staffing models and talent frameworks that match client needs with appropriate skill profiles
- Deploy agentic and AI\-native tooling (e.g. Claude Code, MCP integrations, multi\-agent workflows) inside the delivery function itself to compress reporting, status, and program management overhead by orders of magnitude
Enable Teams and Manage Risk
- Provide coaching, mentorship, and capability development to delivery managers and project leads
- Establish escalation protocols and governance structures for high\-stakes client situations, and operationalize Andela’s enterprise compliance posture (SOC 2, ISO 27001, ISO 42001\) within delivery
- Balance risk management with calculated risk\-taking to accelerate learning and market differentiation
- Foster a culture of transparency, accountability, and continuous improvement across delivery teams
Drive Strategic Partnerships
- Serve as an Executive / Senior Sponsor for strategic enterprise engagements, partnering directly with client executives to shape delivery strategy, mitigate risk, and drive long\-term partnership growth.
- Collaborate with Sales, Customer Success, and Product teams to ensure seamless handoffs and integrated customer experience
- Partner with Talent Fulfillment Operations to ensure availability of properly skilled resources for AI Solutions and custom development engagements
- Work with Finance to refine margin models, pricing strategies, and financial forecasting for delivery operations, and implement new system(s) to automate reporting
What You Bring
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Required Experience
- 10\+ years in delivery management, professional services, or consulting operations, with at least 5 years in leadership roles
- Proven track record building and scaling delivery organizations in high\-growth technology companies ($100M\+ in managed revenue, 30\+ concurrent projects)
- Deep expertise in process design, project governance, and operational excellence methodologies, with the technical fluency to engage credibly on AI\-specific delivery patterns (evals, model deployment, MLOps, agentic systems)
- Demonstrated success managing complex, outcome\-based engagements with Fortune 500 clients
- Strong financial acumen with experience managing P\&L, margin optimization, and resource economics
- Experience in AI/ML solutions delivery or adjacent emerging technology domains (cloud transformation, data engineering, etc.)
Required Capabilities
- Systems Builder: You’ve built delivery frameworks from scratch, bring intellectual rigor to ambiguous problems, and know when to enforce standards versus when to flex. Deep experience with outcome\-based or fixed\-fee delivery models.
- Metrics\-Driven: You live in data, build dashboards others actually use, and can spot patterns before they become problems
- Risk Calibrated: You anticipate failure modes and build mitigation strategies, while also running experiments and failing fast in service of learning. You know which risks to take and which to escalate
- Change Leader: You can drive adoption of new processes in organizations that may resist structure
- Talent Developer: You invest in growing future leaders and treat team\-building with the same craft as system\-building
Preferred Experience
- Experience in professional services or consulting firms (Big 4, MBB, boutique AI consultancies, Hyperscalers)
- Background in AI/ML product development or solutions delivery
- Experience working in startup or scale\-up environments with rapid organizational change
- Technical background or ability to engage credibly with senior engineering leaders
Why This Role Matters
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The diffusion gap is the defining bottleneck of this AI cycle. Enterprises have the models. They don’t have the operational muscle to put them into production at scale. Andela is one of the few companies positioned to close that gap, with partnerships across AI Labs, Hyperscalers and strategy consulting firms, and a global Talent Network that competitors can’t replicate.
What we don’t yet have, at the scale we need it, is the operational engine that turns those advantages into predictable enterprise outcomes. That’s the work. Every framework you build, every playbook you ship, every program manager you develop will directly compound how fast Andela can deliver against this opportunity.
If you're energized by the challenge of bringing structure to ambiguity, building systems that empower rather than constrain, and scaling a business where the playbook is still being written, this role is for you.
\#LI\-REMOTE
\#LI\-RDR
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Andela, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Andela AI Hiring
Andela has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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