
AI Technical Architect
- New York City, NY
- $160,000-183,000 per year
- Permanent
- Full-time
- Define and evolve the enterprise-wide architectural vision and strategy for Generative AI and agentic systems, aligning with overall business goals and technology roadmaps.
- Lead the design of modular, reusable, and scalable architectural patterns for GenAI and agentic applications across various domains.
- Design and implement robust, secure solution patterns that can be operationalized across enterprise environments.
- Deep expertise in various foundation models (e.g., LLMs, vision models, multimodal models) including their architectures, strengths, limitations, and fine-tuning techniques.
- Evaluate, select, and integrate appropriate foundation models for specific use cases, considering factors like performance, cost, and ethical implications.
- Develop strategies for model pre-training, fine-tuning, and continuous improvement.
- Architect and implement intelligent agentic AI systems using frameworks like LangChain, LangGraph, CrewAI, AutoGen, or similar.
- Design complex agentic workflows, including planning, reasoning, tool integration, memory management, and human-in-the-loop interactions.
- Lead the integration of agentic AI solutions with existing enterprise systems and define integration standards (e.g., RESTful APIs, microservices).
- Apply advanced prompt engineering techniques to optimize AI model performance and steer outputs towards desired outcomes, minimizing bias and hallucinations.
- Oversee orchestration of AI components and services, including LLM APIs, vector databases, and external tools.
- Develop and implement Retrieval Augmented Generation (RAG) based solutions for enhanced contextual understanding and factual accuracy.
- Design and build cloud-native, containerized infrastructure (e.g., Kubernetes, ECS, EKS on AWS, Azure, GCP) for deploying GenAI models and agentic systems at scale.
- Implement robust MLOps pipelines for the continuous integration, delivery, deployment, monitoring, and management of AI models in production.
- Ensure AI solutions comply with regulatory requirements, data privacy, and ethical AI standards.
- Stay abreast of the latest advancements in AI, machine learning, deep learning, and agentic systems, and apply this knowledge to drive innovation.
- Rapidly develop Proofs of Concept (PoCs) and iterate solutions using an agile, experimental approach without compromising architectural integrity or long-term scalability.
- Serve as a technical expert and mentor to junior AI engineers and architects, fostering a culture of continuous learning and improvement.
- Contribute to internal workshops, knowledge sharing, and external forums as a thought leader.
- Collaborate effectively with cross-functional teams including product managers, data scientists, software engineers, security, and business stakeholders.
- Articulate complex technical concepts to diverse audiences, both technical and non-technical.
- Maintain detailed architectural documentation and operational playbooks.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- 10+ years of progressive experience in software architecture, with at least 2-3 years specifically focused on Generative AI and Machine Learning.
- Demonstrated expertise in designing and implementing AI architectures with a strong focus on Generative AI and Agentic AI technologies.
- Profound understanding of various foundation models (LLMs, vision models, multimodal models) and their underlying architectures (e.g., Transformers).
- Hands-on experience with agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
- Strong programming skills in Python is essential, with experience in relevant AI/ML frameworks (e.g., TensorFlow, PyTorch).
- Experience with cloud platforms (AWS, Azure, GCP) and their AI/ML services (e.g., Amazon SageMaker, Azure Machine Learning, Google Cloud AI Platform, Vertex AI).
- Familiarity with MLOps principles and tools for deploying, monitoring, and managing AI models and agentic systems in production (e.g., MLflow, Kubeflow).
- Experience with vector databases (e.g., Pinecone, Weaviate, FAISS, Azure AI Search) and techniques for processing and ingesting unstructured data.
- Excellent communication, interpersonal, and leadership skills, with the ability to influence and drive technical decisions.
- Strong analytical and problem-solving abilities.
- Willingness to work in the New York office 3 days per week.