AI Native Product Architect

AI Native Product Architect

Join NTT DATA North America as an AI Native Product Architect in Plano, Texas.

• 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, Tensor Flow, Hugging Face, Lang Chain, 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 Sage Maker, 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.

• 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, Tensor Flow, Hugging Face, Lang Chain).
  • 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.