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About This Role
Req ID: 382452
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward\-thinking organization, apply now.
NTT DATA's Client is seeking a Senior AI Ops / DevOps Engineer to join their team in Atlanta, Georgia (US\-GA), United States (US).
The Senior AI Ops / DevOps Engineer will architect, build, and manage next\-generation AI\-driven CI/CD and cloud operations ecosystems. This role will go beyond traditional DevOps automation by integrating LLM agents, Model Context Protocol servers, intelligent observability, and secure AI\-assisted workflows into the software delivery lifecycle.
- Architect, build, and manage AI\-enabled CI/CD pipelines that improve developer productivity, code quality, release reliability, and deployment speed.
- Design and deploy production\-grade Model Context Protocol clients and servers to securely connect enterprise LLMs with engineering tools, repositories, cloud infrastructure, and observability platforms.
- Develop custom MCP servers using Python, TypeScript, Node.js, or JavaScript to expose logs, infrastructure metrics, deployment data, and internal tools to authorized AI agents.
- Integrate LLM agents into developer workflows to support automated code review, vulnerability detection, test generation, release validation, and infrastructure recommendations.
- Build and maintain robust CI/CD pipelines using GitHub Actions, GitLab CI, CircleCI, ArgoCD, Jenkins, or similar tools.
- Implement ChatOps 2\.0 capabilities that allow engineers to interact with deployment pipelines, cloud environments, logs, and operational workflows using secure conversational interfaces.
- Create safe autonomous remediation workflows for log analysis, incident triage, root\-cause analysis, and infrastructure issue resolution.
- Build guardrails that allow AI agents to generate, inspect, and safely execute Infrastructure as Code using Terraform, OpenTofu, Terragrunt, Pulumi, Crossplane, or similar tools.
- Manage containerized workloads using Docker and Kubernetes platforms such as AWS EKS, Azure AKS, or Google GKE.
- Integrate AI\-driven observability workflows with platforms such as Datadog, Prometheus, Grafana, CloudWatch, Splunk, Dynatrace, or ELK.
- Implement AI safety controls including role\-based access control, least\-privilege execution, human\-in\-the\-loop approvals, audit logging, rollback mechanisms, and secure tool access.
- Partner with software engineering, DevOps, SRE, security, platform, and data/AI teams to identify opportunities for intelligent automation.
- Create reusable automation frameworks, runbooks, dashboards, documentation, and enablement materials for engineering teams.
- Drive an "automate everything” culture by reducing manual toil and improving operational efficiency across cloud and software delivery processes.
Basic Qualifications
- Minimum 7\+ years of experience in DevOps, Cloud Engineering, SRE, Platform Engineering, or Infrastructure Automation.
- Minimum 4\+ years of hands\-on experience designing and managing CI/CD pipelines using GitHub Actions, GitLab CI, CircleCI, Jenkins, ArgoCD, or similar platforms.
- Minimum 3\+ years of experience managing scalable cloud environments in AWS, Azure, or GCP, with strong preference for AWS.
- Minimum 3\+ years of Strong hands\-on experience with Kubernetes, Docker, and production container orchestration platforms such as EKS, AKS, or GKE.
- Advanced proficiency with Infrastructure as Code tools such as Terraform, OpenTofu, Terragrunt, Pulumi, CloudFormation, or Crossplane.
- Minimum 3\+ years of Strong programming and scripting experience using Python, TypeScript, JavaScript, Bash, or Go.
- Minimum 3\+ years of Practical experience working with LLM APIs such as OpenAI, Anthropic, or similar enterprise AI platforms.
- Minimum 3\+ years of Experience with AI orchestration or agentic frameworks such as LangChain, CrewAI, LlamaIndex, or similar tools.
- Minimum 3\+ years of Strong understanding of the Model Context Protocol ecosystem and experience designing or integrating MCP clients and servers.
- Minimum 3\+ years of Experience integrating DevSecOps controls into CI/CD pipelines, including SAST, DAST, dependency scanning, container scanning, secrets scanning, and vulnerability management.
- Minimum 3\+ years of Strong knowledge of secret management and security tooling such as HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, or similar platforms.
- Minimum 3\+ years of Experience with observability, monitoring, logging, and alerting platforms such as Datadog, Prometheus, Grafana, CloudWatch, Splunk, Dynatrace, or ELK.
- Familiarity with security and compliance frameworks such as SOC2, ISO27001, or enterprise audit control environments.
- Ability to troubleshoot complex pipeline, infrastructure, deployment, and production issues across cloud\-native environments.
Preferred / Nice to Have
- Experience building AI\-assisted infrastructure provisioning workflows.
- Experience implementing autonomous or semi\-autonomous incident response and remediation capabilities.
- Experience with MLOps, model deployment pipelines, model monitoring, MLflow, SageMaker, or equivalent platforms.
- Experience implementing human\-in\-the\-loop approval models for AI\-generated operational actions.
- Experience with policy\-as\-code tools such as Open Policy Agent, Sentinel, Checkov, or similar solutions.
- Experience working in regulated industries such as banking, financial services, healthcare, or insurance.
- Experience with GitOps operating models using ArgoCD, Flux, or similar tools.
- AWS, Kubernetes, DevOps, Security, or AI/ML certifications are a plus.
- Soft Skills \& Mindset
- Strong "automate everything” mindset with a passion for reducing repetitive manual tasks and operational toil.
- Security\-first approach with practical skepticism of autonomous AI actions and a focus on validation, boundaries, approvals, and rollback.
- Ability to bridge traditional software engineering, DevOps, SRE, security, and data/AI teams.
- Strong communication skills with the ability to explain complex AI\-enabled DevOps concepts to both technical and leadership audiences.
- Collaborative educator who can help upskill engineering teams on AI\-assisted delivery, secure automation, and modern DevOps practices.
- Ownership mindset with the ability to design solutions, implement them hands\-on, and support them in production.
Travel
- This position requires 3 days in office per the client/project requirement.
Degree
- Bachelor's degree in Computer Science, Engineering, Information Technology, or equivalent work experience.
Where required by law, NTT DATA provides a reasonable range of compensation for specific roles. The starting hourly range for this remote role is ($60\-$70/hour). This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on several factors, including the candidate's actual work location, relevant experience, technical skills, and other qualifications. This position may also be eligible for incentive compensation based on individual and/or company performance.
This position is eligible for company benefits that will depend on the nature of the role offered. Company benefits may include medical, dental, and vision insurance, flexible spending or health savings account, life, and AD\&D insurance, short\-and long\-term disability coverage, paid time off, employee assistance, participation in a 401k program with company match, and additional voluntary or legally required benefits*.*
About NTT DATA:
NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start\-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3\.6 billion each year in R\&D to help organizations and society move confidently and sustainably into the digital future. Visit us at us.nttdata.com
NTT DATA endeavors to make https://us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at https://us.nttdata.com/en/contact\-us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you'd like more information on your EEO rights under the law, please click here. For Pay Transparency information, please click here.
Salary Context
This $124K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 NTT DATA, 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. This role's midpoint ($135K) sits 38% below the category median. Disclosed range: $124K to $145K.
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.
NTT DATA AI Hiring
NTT DATA has 14 open AI roles right now. They're hiring across AI Consultant, AI/ML Engineer, AI Architect, Prompt Engineer. Positions span Plano, TX, US, Atlanta, GA, US, Irving, TX, US. Compensation range: $114K - $426K.
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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