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# AI Systems Engineer (Agentic & LLM Production)

- **Company:** NTT DATA, Inc.
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Salary:** $104,000.0 - $124,800.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Amazon S3, C++ (Programming Language), Cloud Computing, Data as a Services, Data Structures, Amazon DynamoDB, Information Retrieval, Python (Programming Language), Machine Learning, Open Source Technology, OpenAI, Software Engineering, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Qwen, Caching, Agentic-AI, Fastapi, Machine Learning Operations, Claude, Invoking Functions, Google Gemini, Data Pipelines, Serverless Computing, Golang
- **Published:** October 13, 2026
- **Apply:** https://www.juju.com/job/1_ntt2-31921872

## About the Role

strategies; instrument continuous evaluations for usefulness, correctness, and risk. + Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load. + Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness. + Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns. **Role Requirements** **Essential Skills** + 5 years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred. + 3 years designing, architecting, testing, and launching production ML systems, including
model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows. + Practical experience with Large Language Models (LLMs): API integration, prompt engineering, fine-tuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution). + Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude). + Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions. + Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact. + Preferred: Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB

## Description

Engineer robust guardrails for safety, compliance, and least-privilege access. + Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations. + Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability. + Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes. + Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback