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About This Role
Company Description
Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change.
By combining world\-class engineering, industry expertise and a people\-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses.
From prototype to real\-world impact \- be part of a global shift by doing work that matters.
Job Description
We're seeking an AI Operations Engineer with experience building AI\-powered applications using Python and Google Cloud Platform (GCP). In this role, you'll design, develop, deploy, and support intelligent AI agents that improve business and operational processes while working with modern cloud infrastructure and emerging agentic AI technologies.
- Design, develop, and maintain AI applications and intelligent agents using Python and the Google Agent Development Kit (ADK).
- Deploy, manage, monitor, and optimize AI workloads within Google Cloud Platform (GCP).
- Build scalable, secure, and maintainable cloud\-native AI services.
- Partner with business stakeholders, architects, software engineers, and data teams to translate business requirements into AI\-enabled solutions.
- Integrate AI capabilities into existing enterprise applications and business workflows.
- Develop and maintain Infrastructure as Code (IaC) using Terraform to support automated cloud deployments.
- Troubleshoot production issues, optimize application performance, and continuously improve reliability and scalability.
- Participate in Agile ceremonies, sprint planning, code reviews, and technical design discussions.
- Contribute to engineering best practices, reusable frameworks, and AI development standards.
Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related technical discipline (or equivalent practical experience).
- 2–4 years of professional software engineering experience.
- Strong programming skills in Python.
- Hands\-on experience developing AI applications using the Google Agent Development Kit (ADK) or similar agentic AI frameworks.
- Experience developing and deploying applications within Google Cloud Platform (GCP).
- Experience working with REST APIs, cloud services, and distributed applications.
- Strong analytical and problem\-solving skills.
- Excellent communication and collaboration skills within Agile teams.
Preferred Qualifications
- Experience with Terraform and Infrastructure as Code (IaC).
- Experience with containerization technologies such as Docker or Kubernetes.
- Familiarity with CI/CD pipelines and DevOps practices.
- Mobile development experience (Android and/or iOS).
- Experience integrating AI models into enterprise applications.
- Knowledge of software testing, monitoring, and production support best practices.
One or more of the following certifications is a plus:
- Google Associate Cloud Engineer
- Google Professional Cloud Developer
- Google Professional Cloud Architect
Additional Information
Discover some of the global benefits that empower our people to become the best version of themselves:
- Finance: Competitive salary package, share plan, company performance bonuses, value\-based recognition awards, referral bonus;
- Career Development: Career coaching, global career opportunities, non\-linear career paths, internal development programmes for management and technical leadership;
- Learning Opportunities: Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass\-it\-on sessions, workshops, conferences;
- Work\-Life Balance: Hybrid work and flexible working hours, employee assistance programme;
- Health: Global internal wellbeing programme, access to wellbeing apps;
- Community: Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations.
Additional Employee Requirements
- Participation in both internal meetings and external meetings via video calls, as necessary.
- Ability to go into corporate or client offices to work onsite, as necessary.
- Prolonged periods of remaining stationary at a desk and working on a computer, as necessary.
- Ability to bend, kneel, crouch, and reach overhead, as necessary.
- Hand\-eye coordination necessary to operate computers and various pieces of office equipment, as necessary.
- Vision abilities including close vision, toleration of fluorescent lighting, and adjusting focus, as necessary.
- For positions that require business travel and/or event attendance, ability to lift 25 lbs, as necessary.
- For positions that require business travel and/or event attendance, a valid driver’s license and acceptable driving record are required, as driving is an essential job function.
- If requested, reasonable accommodations will be made to enable employees requiring accommodations to perform the essential functions of their jobs, absent undue hardship.
USA Benefits (Full time roles only, does not apply to contractor positions)
- Robust healthcare and benefits including Medical, Dental, vision, Disability coverage, and various other benefit options
- Flexible Spending Accounts (Medical, Transit, and Dependent Care)
- Employer Paid Life Insurance and AD\&D Coverages
- Health Savings account paired with our low\-cost High Deductible Medical Plan
- 401(k) Safe Harbor Retirement plan with employer match with immediately vest
At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At endava, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
endava AI Hiring
endava has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in CO, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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