Artificial Intelligence Architect (AI Architect)

Job Description

We are seeking a highly skilled AI Architect to lead our team in developing cutting-edge generative AI solutions. This role requires a deep understanding of AI/ML concepts, strong technical expertise, and exceptional leadership abilities. As the Lead AI Architect, you will be the technical authority responsible for defining the strategy, architecture, and execution of cutting-edge generative AI initiatives. You will lead a talented team, driving the development of innovative solutions from concept through production deployment. This role demands a deep understanding of state-of-the-art AI models, advanced techniques like Retrieval-Augmented Generation (RAG) and fine-tuning, robust MLOps practices, and scalable cloud deployment strategies. Responsibilities: 1. Technical Vision & Architecture: a. Define and champion the technical vision and architectural roadmap for generative AI solutions (LLMs, Diffusion Models, Multimodal Models, etc.). b. Design, architect, and guide the development of sophisticated generative AI systems, incorporating techniques like fine-tuning, prompt engineering, and Reinforcement Learning from Human Feedback (RLHF). c. Architect and implement advanced Retrieval-Augmented Generation (RAG) pipelines, including vector database selection/optimization, chunking strategies, and multimodal RAG approaches. d. Ensure solutions are scalable, performant, cost-effective, reliable, and adhere to responsible AI principles (fairness, transparency, security). e. Stay relentlessly current with academic research and industry advancements in generative AI, MLOps, and related technologies, integrating relevant innovations. 2. MLOps & Lifecycle Management: a. Lead the design and implementation of robust MLOps pipelines for the entire generative AI lifecycle: data ingestion/preprocessing, model training/fine-tuning, validation, versioning (data, code, models), automated deployment, and continuous monitoring. Job Opening b. Establish best practices for model evaluation, performance tracking, drift detection, and ongoing optimization of generative models in production. c. Champion automation, reproducibility, and governance across all AI/ML workflows. 3. Deployment & Integration : a. Oversee the deployment of fine-tuned models and complex AI systems into scalable production environments using cloud platforms (AWS, GCP, Azure, potentially NVIDIA AI Enterprise). b. Design and implement containerized deployments (Docker, Kubernetes) and serverless architectures for AI model serving. c. Develop robust APIs (RESTful, gRPC) for seamless integration of AI capabilities into broader applications and services. d. Optimize models for inference speed and cost efficiency. 4. Leadership & Collaboration : a. Lead, mentor, and inspire a team of AI/ML engineers and data scientists. b. Collaborate closely with product managers, software engineers, data engineers, and business stakeholders to translate requirements into actionable AI strategies and technical designs. c. Effectively communicate complex technical concepts, architectural decisions, and project status to both technical and non-technical audiences, including executive leadership. d. Foster a culture of innovation, experimentation, and continuous improvement within the AI team. Qualifications : • Typically 8-10+ years of hands-on experience in designing, building, and deploying production-grade AI/ML systems, with a significant recent focus (3+ years) on Generative AI. • Generative AI Expertise: Deep understanding and practical experience with foundational models (e.g., GPT series, Llama series, Stable Diffusion, multimodal models), transformer architectures, and key techniques like fine-tuning, prompt engineering, and RAG. • RAG Proficiency: Proven experience architecting and implementing RAG systems, including familiarity with vector databases (e.g., Pinecone, Weaviate, Milvus, ChromaDB) and embedding techniques. • MLOps Mastery: Demonstrable experience designing, building, and managing end-to-end MLOps pipelines using relevant tools and platforms (e.g., MLflow, Kubeflow, Vertex AI Pipelines, SageMaker Pipelines, Azure ML). • Programming & Frameworks: Expert-level proficiency in Python and core AI/ML libraries/frameworks (e.g., PyTorch, TensorFlow/Keras, JAX, Hugging Face ecosystem, LangChain, LlamaIndex). • Cloud Platforms: Strong hands-on experience with at least one major cloud provider (AWS, GCP, Azure) and their associated AI/ML services (e.g., SageMaker, Vertex AI, Azure Machine Learning). • Deployment Skills: Experience with containerization (Docker, Kubernetes), API development (REST, gRPC), and model serving/optimization techniques. • Leadership: Proven track record of technical leadership, mentoring teams, and successfully delivering complex AI projects from ideation to production. • Communication: Excellent verbal and written communication skills, with the ability to articulate complex technical ideas clearly and concisely. • Methodologies: Familiarity with Agile development practices and version control systems (Git). • Education: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related quantitative field. (PhD preferred but not required). Preferred Qualifications: • Education: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related quantitative field. (PhD preferred but not required). • Experience with multimodal generative models and RAG. • Experience with distributed training and large-scale model optimization (e.g., quantization, pruning). • Familiarity with data engineering tools and platforms (e.g., Spark, Kafka, Airflow). • Contributions to relevant open-source projects or publications in top AI/ML venues.