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About This Role
Company Overview:
Req ID: 383751
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 currently seeking a Senior AI/LLM Engineer to join their team in Pittsburgh, Pennsylvania (US\-PA), United States (US). REMOTE
Job Description – Senior AI/LLM Engineer (Generative AI Solutions)
Job Title: Senior AI/LLM Engineer – Generative AI Solutions
Experience: 6–10\+ Years (Minimum 3\+ Years in AI/Generative AI)
Location: Remote \- Need to support EST timezone.
Project Overview
We are seeking an experienced Senior AI/LLM Engineer to design, develop, and deploy enterprise\-grade Generative AI solutions that leverage Large Language Models (LLMs) to solve complex business challenges. The ideal candidate will have strong expertise in Python development, Retrieval\-Augmented Generation (RAG), AI orchestration frameworks, and cloud\-native AI architectures.
This role will work closely with product owners, solution architects, data engineers, and business stakeholders to build scalable, secure, and production\-ready AI applications powered by OpenAI and other leading foundation models.
Key Responsibilities
AI Solution Design \& Development
- Design, develop, and deploy enterprise\-scale Generative AI applications using modern LLM technologies.
- Build production\-grade backend services using Python and modern software engineering practices.
- Develop scalable AI architectures utilizing OpenAI, Anthropic Claude, Gemini, Llama, or similar foundation models.
- Design and implement Retrieval\-Augmented Generation (RAG) pipelines using vector databases and semantic search capabilities.
- Develop intelligent multi\-agent AI systems capable of orchestrating complex business workflows.
AI Architecture \& Integration
- Design AI solution architectures that are scalable, secure, maintainable, and aligned with enterprise standards.
- Integrate AI capabilities into existing enterprise applications, APIs, and business workflows.
- Develop and consume REST APIs, microservices, and event\-driven services for AI applications.
- Implement AI orchestration frameworks such as LangChain and related agent frameworks.
Prompt Engineering \& Model Optimization
- Develop and optimize prompts for improved accuracy, reasoning, and business outcomes.
- Evaluate LLM performance and implement techniques to improve response quality.
- Establish AI governance, model evaluation, and responsible AI best practices.
- Monitor AI application performance and continuously optimize latency, cost, and quality.
Cloud \& Enterprise AI
- Build cloud\-native AI solutions using Azure or AWS AI services.
- Implement Azure AI Search and vector search capabilities.
- Design secure enterprise AI applications following cloud security and governance standards.
- Collaborate with DevOps teams to deploy AI solutions using CI/CD pipelines.
- Stakeholder Collaboration
- Partner with business stakeholders to understand AI use cases and translate them into scalable technical solutions.
- Present architecture decisions, solution approaches, and AI strategies to both technical and non\-technical audiences.
- Mentor junior engineers and contribute to AI engineering best practices across the organization.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
- 6\+ years of software engineering or machine learning engineering experience.
- Minimum 3\+ years of hands\-on experience building Artificial Intelligence and Generative AI solutions.
- 3 to 5 years of strong hands\-on experience developing production applications using Python.
- 1 to 3 years of experience building applications using OpenAI (preferred), Anthropic Claude, Gemini, Llama, or similar LLM platforms.
- 3\+ years of strong experience implementing Retrieval\-Augmented Generation (RAG) architectures.
- 1 to 3 years of experience integrating vector databases and semantic search solutions.
- 1 to 3 years of strong understanding of LangChain and AI orchestration frameworks.
- Experience designing multi\-agent AI architectures.
- Strong knowledge of prompt engineering, model evaluation, AI governance, and Responsible AI principles.
- Experience building REST APIs, microservices, and event\-driven architectures.
- Experience with Azure or AWS cloud platforms.
- Strong understanding of scalable enterprise application architecture.
- Excellent analytical, problem\-solving, and communication skills.
Required Technical Skills
- Python
- JavaScript
- Generative AI
- Large Language Models (LLMs)
- OpenAI (Preferred)
- Retrieval\-Augmented Generation (RAG)
- Multi\-Agent AI Orchestration
- LangChain
- Langfuse
- AI Solution Architecture
- Azure AI Search
- Vector Databases
- Prompt Engineering
- REST APIs
- Microservices
- Event\-Driven Architecture
- Azure or AWS Cloud Services
Preferred Qualifications
- Experience with LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar AI agent frameworks.
- Experience with vector databases such as Pinecone, Weaviate, ChromaDB, Qdrant, Milvus, or Azure AI Search.
- Experience with containerization technologies such as Docker and Kubernetes.
- Knowledge of CI/CD pipelines and MLOps practices.
- Experience with AI observability and monitoring platforms.
- Familiarity with enterprise security, compliance, and Responsible AI frameworks.
- Experience working in Agile/Scrum environments.
Nice to Have
- Experience developing enterprise copilots or AI assistants.
- Experience integrating AI into enterprise SaaS platforms.
- Knowledge of AI governance, security, and compliance standards.
- Experience optimizing LLM inference performance and AI operational costs.
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.
NTT DATA provides a reasonable range of compensation for U.S.\-based positions. The starting pay range for this remote role is $55 to $62/hour. This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on a number of factors, including the candidate's actual work location, relevant experience, technical skills, and other qualifications.
This position is eligible for company benefits including participation in medical, dental, and vision insurance, flexible spending or health savings account, and AD\&D insurance, employee assistance, participation in a 401k program, and additional voluntary or legally\-required benefits
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\#li\-northamerica
Salary Context
This $114K-$128K range is in the lower quartile for LLM Engineer roles in our dataset (median: $156K across 8 roles with salary data).
View full LLM Engineer salary data →Role Details
About This Role
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 4,317 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At NTT DATA, this role fits into their broader AI and engineering organization.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
Skills Required
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Compensation Benchmarks
LLM Engineer roles pay a median of $200,500 based on 18 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($121K) sits 39% below the category median. Disclosed range: $114K to $128K.
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.
NTT DATA AI Hiring
NTT DATA has 13 open AI roles right now. They're hiring across AI/ML Engineer, LLM Engineer, AI Architect. Positions span Plano, TX, US, Dallas, TX, US, TX, US. Compensation range: $128K - $450K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture 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).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
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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