Job description

The Role: GenAI Solutions Architect - Assoc Director/Director Responsibilities include: • GenAI Architecture Strategy: Develop and implement AI architecture strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency. Develop architecture roadmap and strategy for GenAI Platforms and tech stacks • ML Architecture Design and Development: Responsible for the design and development of custom AI architecture for batch and stream processing-based AI ML pipelines, including data ingestion to preprocessing to scaled AI model computes and ensuring the architecture meets all SLA requirements. Work closely with members of technology and business teams in the design, development, and implementation of Enterprise AI platform • Internal Collaboration: Collaborate closely with data scientists, machine learning engineers, and software engineers to ensure smooth integration of machine learning models into production systems. • Stakeholder Engagement and Collaboration: Collaborate closely with business and PM stakeholders in roadmap planning and implementation efforts and ensure technical milestones align with business requirements. • AI Infrastructure Architecture: Oversee the design of scalable and reliable infrastructure for AI, ML, GenAI, and LLM model training and deployment. • AI Model Deployment Architecture: Lead the architecture of GenAI, LLM, and machine learning model deployment patterns in production environments, with design patterns that ensure reliability and scalability. • AI Monitoring Architecture: Create design of GenAI robust monitoring systems to track model performance, data quality, and infrastructure. • Security and Compliance: Implement security measures and compliance standards to protect sensitive data and ensure adherence to industry regulations. • Documentation: Maintain comprehensive documentation of AI processes and procedures for reference and knowledge sharing. • Standards and Best Practices: Ensure the use of standards, governance, and best practices in AI pipeline monitoring and ML model monitoring, and adherence to model and data governance standards • Problem Solving: Troubleshoot complex issues related to machine learning model deployments and data pipelines and develop innovative solutions. • Serve as a thought leader in generative AI, influencing both technical strategy and executive-level decisions. • Build and scale production-ready AI systems that operate reliably at enterprise levels, ensuring long-term business impact. • Exceptional presentation skills to convey technical concepts to non-technical stakeholders. • Ability to adapt communication styles to various audiences, from engineers to business stakeholders and executive leadership. • Passion for staying ahead of AI trends and leveraging emerging technologies. • Strategic thinker and influencer with demonstrated technical and business acumen and problem-solving skills What We’re Looking For: Basic Required Qualifications: • Bachelor's or Master's degree in Computer Science, Engineering, or a related field. • Experienced professional (8+ years experience) as ML engineer, architect, and lead data scientist in a Big Data ecosystem or any similar distributed or public cloud platform, with a desire to assume greater responsibilities as a leader and mentor while still being hands-on • 4+ years of hands-on experience in ML architecture design and implementation for large-scale enterprise AI solutions and AI products • 4+ years of experience with Business, Product Stakeholder Engagement, and Collaboration: Demonstrated success Collaborating with business, product, and PM stakeholders in AI roadmap planning and implementation efforts and ensuring technical milestones align with business requirements. • Experience working in Agile frameworks and delivery methods (scaled Agile, SAFe, etc.). • Expertise (4+ years) in the design and development of complex data-driven architectures for distributed computing and orchestration technology (Kubernetes, Ray, Airflow) and scaling • Experience with public cloud platforms & system architectures (AWS, GCP, Azure) • Proficiency with Databricks, MLflow, Flink, or similar AI/ML/ML technologies • Experience with SQL, NoSQL, ElasticSearch, MongoDB, and Spark, Python, PySpark for model development and ML Ops • Knowledge of DevOps, MLOps principles and practices, and experience with version control systems (e.g., Git) and CI/CD pipelines. • Strong familiarity with higher-level trends in LLMs and open-source platforms. Additional Preferred Qualifications: • Experience with contributing to GitHub and open source initiatives or in research projects

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