Senior AI/ML Architect

Responsibilities

• Architect and oversee the development of robust, scalable systems using generative AI models.
• Collaborate with stakeholders to define business requirements and technical specifications for generative AI applications.
• Guide the selection, customization, and optimization of state-of-the-art generative AI models.
• Design a system to maintain deployed solutions at customer sites.
• Develop end-to-end pipelines for inference and monitoring in production environments.
• Ensure systems meet high standards for performance, scalability, and security while adhering to data privacy regulations.
• Lead the implementation of APIs, microservices, and frameworks to integrate AI models into enterprise solutions.
• Provide mentorship to engineering teams, fostering expertise in AI and software architecture.
• Design and maintain platforms to handle large-scale solution applications in collaboration with legal/compliance teams.
• Define architectural best practices to mitigate risks associated with Generative AI (e.g., model hallucinations).
• Align AI architecture with organizational goals and contribute to strategic technology roadmaps.
• Document architectural designs, workflows, and decisions for transparency and scalability.

Requirements

• Bachelor’s degree or higher in a relevant discipline.
• 8+ years of experience within software engineering or a related field.

Desired

• Deep understanding of LLM integration patterns (RAG, Agents, Tool-use) and Prompt Engineering strategies.
• Expertise in designing scalable, distributed architectures for AI systems.
• Strong experience with cloud computing platforms (AWS, Azure, GCP) and containerization (Kubernetes, Docker).
• Familiarity with large-scale distributed systems and database technologies.
• Experience in creating technical design documents and implementation playbooks for target-state AI solutions within cloud environments.
• Knowledge of integration platforms and protocols (e.g., REST, SOAP, HTTP, UDP, etc.).
• Proficiency in designing RESTful APIs and GraphQL endpoints for AI services.
• Knowledge of API development, microservices architecture, and DevOps practices.
• Proficiency in MLOps/LLMOps and model lifecycle management, including CI/CD pipelines for training, testing, and deploying AI models at scale.
• Performance optimization for AI/ML workloads, including GPU/TPU acceleration, model quantization, pruning, and distillation.
• Observability & Monitoring of AI pipelines, encompassing logging, tracing, and metrics to detect drift, anomalies, or performance bottlenecks.
• Security, Privacy, and Compliance knowledge, with an understanding of data governance (GDPR, HIPAA, SOC 2) and secure model serving.
• Proven track record of designing scalable Generative AI use case solutions.
• Exceptional leadership, strategic thinking, and problem-solving abilities.
• Excellent communication skills for engaging with stakeholders across technical and business domains.
• Strong oral and written communication skills.
• Strong interpersonal and leadership skills.
• Demonstrated ability to analyze and resolve problems.
• Demonstrated ability to lead programs / projects.
• Ability to document, plan, market, and execute programs.