GenAI Solutions Architect

GenAI Solutions Architect - Assoc Dir/Dir This range is provided by Aegistech. Your actual pay will be based on your skills and experience talk with your recruiter to learn more. Base pay range $150,000.00/yr - $210,000.00/yr Direct message the job poster from Aegistech 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: Design and develop custom AI architecture for batch and stream processing-based AI ML pipelines, including data ingestion to preprocessing to scaled AI modelCompute and ensuring the architecture meets all SLA requirements. Collaborate with technology and business teams to design, develop, and implement Enterprise AI platform. Internal Collaboration: Collaborate with data scientists, machine learning engineers, and software engineers to ensure smooth integration of ML models into production systems. Stakeholder Engagement and Collaboration: Work with business and PM stakeholders in roadmap planning and implementation efforts to 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 GenAI, LLM, and ML model deployment patterns in production environments with scalable and reliable design patterns. AI Monitoring Architecture: Create designs for 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 ML deployments and data pipelines, and develop innovative solutions. Thought Leadership: Serve as a thought leader in generative AI, influencing technical strategy and executive-level decisions. Production-grade AI Systems: Build and scale production-ready AI systems that operate reliably at enterprise levels, delivering long-term business impact. Communication: Exhibit exceptional presentation skills to convey technical concepts to non-technical stakeholders and adapt communication styles for engineers, business stakeholders, and executives. Continuous Learning: Stay ahead of AI trends and leverage emerging technologies. Strategic Influence: Demonstrate technical and business acumen with problem-solving skills to guide strategy. What Were Looking For: Basic Required Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 8+ years of experience as ML engineer, architect, and lead data scientist in a Big Data ecosystem or similar distributed/public cloud platform, with hands-on leadership responsibilities. 4+ years of hands-on experience in ML architecture design and implementation for large-scale enterprise AI solutions and products. 4+ years of experience with business, product stakeholder engagement, and collaboration to align AI roadmaps with business requirements. Experience working in Agile frameworks and delivery methods (scaled Agile, SAFe, etc.). 4+ years of design and development experience in complex data-driven architectures for distributed computing and orchestration technologies (Kubernetes, Ray, Airflow). Experience with public cloud platforms and system architectures (AWS, GCP, Azure). Proficiency with Databricks, MLflow, Flink, or similar AI/ML technologies. Experience with SQL, NoSQL, Elasticsearch, MongoDB, Spark, Python, PySpark for model development and MLOps. Knowledge of DevOps, MLOps principles, version control (e.g., Git), and CI/CD pipelines. Strong familiarity with higher-level trends in LLMs and open-source platforms. Additional Preferred Qualifications: Experience contributing to GitHub/open source initiatives or research projects. Seniority level Mid-Senior level Employment type Full-time Job function Information Technology New York, United States #J-18808-Ljbffr