AI Native Product Architect at NTT DATA

Plano, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

06 Jan, 26

Salary

0.0

Posted On

08 Oct, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI-Native Product Architecture, Cloud-Native Systems, MLOps Pipelines, Data Engineering, APIs Development, AI/ML Frameworks, Containerized Architectures, Security and Compliance, Real-Time Inference, Model Training, Performance Benchmarking, Mentoring, Scalable Data Pipelines, Vector Databases, Microservices, Streaming Platforms

Industry

IT Services and IT Consulting

Description

Architecture & Solution Design 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. Hands-On Technical Execution 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. Technology Evaluation & Integration 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. Collaboration & Leadership 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. Education: 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. 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. Collaborate with product managers, AI researchers, and engineers to translate requirements into architecture and conduct technical deep-dives.