NTT DATA North America
AI Native Product Architect
Join NTT DATA North America as an AI Native Product Architect in Plano, Texas.
Key Responsibilities
Architect scalable data pipelines, model training, inference services, and orchestration frameworks.
Design cloud-native, containerized architectures (Kubernetes, microservices, serverless functions) optimized for AI workloads.
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.
Translate product requirements into architecture and conduct technical deep-dives, architecture reviews, and performance benchmarking.
Mentor engineers on AI-native design principles and best practices.
Required Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
Experience: 8+ years in software architecture/engineering, with 4+ years in AI/ML-focused product development.
Technical Expertise:
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.
Preferred Skills:
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.
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 Pay Transparency information, please click here. #J-18808-Ljbffr
Key Responsibilities
Architect scalable data pipelines, model training, inference services, and orchestration frameworks.
Design cloud-native, containerized architectures (Kubernetes, microservices, serverless functions) optimized for AI workloads.
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.
Translate product requirements into architecture and conduct technical deep-dives, architecture reviews, and performance benchmarking.
Mentor engineers on AI-native design principles and best practices.
Required Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
Experience: 8+ years in software architecture/engineering, with 4+ years in AI/ML-focused product development.
Technical Expertise:
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.
Preferred Skills:
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.
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 Pay Transparency information, please click here. #J-18808-Ljbffr