AI Native Product Architect

NTT DATA•Plano, TX

51d

About The Position

We are looking for a highly skilled AI Product Native Architect to design and implement end-to-end AI-native product architectures. This role demands strong technical expertise in AI/ML frameworks, data engineering, cloud-native systems, and scalable distributed architecture, with hands-on experience building and deploying AI-powered products. The ideal candidate is a practitioner-architect who can move seamlessly from designing high-level architecture to rolling up their sleeves and prototyping solutions.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, or related field.
8+ years in software architecture/engineering, with 4+ years in AI/ML-focused product development.
Proven hands-on experience in designing and deploying AI-native systems in production.
Strong proficiency in Python, Java, or Go, with hands-on coding ability.
Deep knowledge of AI/ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain).
Experience with data engineering, ETL pipelines, and streaming platforms (Kafka, Spark, Flink).
Strong understanding of cloud-native systems (Kubernetes, Docker, microservices).
Practical knowledge of vector search, embeddings, retrieval-augmented generation (RAG).
Strong grasp of security, governance, and compliance in AI workloads.

Nice To Haves

Experience scaling LLM-powered applications with low-latency serving and caching strategies.
Knowledge of distributed training/inference using GPUs/TPUs, model sharding, and parallelization.
Familiarity with responsible AI practices: fairness, explainability, auditability.
Exposure to API design and monetization strategies for AI-powered SaaS products

Responsibilities

Define and own the technical architecture of AI-native products, ensuring high availability, performance, and security.
Architect scalable data pipelines, model training, inference services, and orchestration frameworks.
Design cloud-native, containerized architectures (Kubernetes, microservices, serverless functions) optimized for AI workloads.
Create reference architectures and reusable design patterns for AI-first product development.
Build PoCs, prototypes, and reference implementations to validate architecture decisions.
Develop and optimize APIs, vector databases, and real-time inference pipelines for LLMs and ML models.
Implement MLOps pipelines for continuous integration, delivery, monitoring, and retraining of models.
Ensure observability with logging, monitoring, and tracing for data and AI services.
Evaluate AI/ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, LangChain, Ray, MLflow) for product suitability.
Select and integrate data platforms, feature stores, vector DBs (Pinecone, Weaviate, FAISS, Milvus, etc.).
Work with cloud AI services (AWS Sagemaker, Azure AI, GCP Vertex AI) and open-source alternatives.
Optimize cost, latency, and scalability for inference at production scale.
Work closely with product managers, AI researchers, and engineers to translate requirements into architecture.
Conduct technical deep-dives, architecture reviews, and performance benchmarking.
Mentor engineers on AI-native design principles and best practices.