Key Responsibilities
Lead the architecture, design, and implementation of enterprise-scale Agentic AI platforms and intelligent automation solutions that support critical business functions and data-driven decision making.
Define and drive the organization's AI/ML architecture strategy, reference architectures, standards, and best practices across data, model, infrastructure, and governance layers.
Design and implement advanced multi-agent systems using frameworks such as LangGraph, AutoGen, LlamaIndex, and emerging agent orchestration technologies.
Architect complex Retrieval-Augmented Generation (RAG) solutions leveraging vector databases, knowledge graphs, semantic search, and enterprise data platforms.
Develop scalable agentic workflows incorporating tool-calling, memory management, planning, reasoning, reflection, and human-in-the-loop capabilities.
Lead the integration of LLM-powered applications utilizing Azure OpenAI, AWS Bedrock, and other enterprise AI services while ensuring performance, security, and cost optimization.
Design enterprise-grade data architectures utilizing Snowflake, Apache Iceberg, Vector Databases, and cloud-native data platforms to support AI and analytics workloads.
Establish standards and frameworks for model lifecycle management, experimentation, deployment, monitoring, observability, and governance using MLflow and modern MLOps practices.
Define and implement enterprise AI security controls, including OAuth authentication, fine-grained authorization, role-based access control, data protection, and model governance frameworks.
Drive AI platform modernization initiatives through containerization and orchestration technologies including Docker, Kubernetes, and cloud-native deployment architectures.
Establish reusable patterns for Model Context Protocol (MCP) integration, external tool orchestration, AI application interoperability, and agent communication frameworks.
Collaborate with business leaders, product owners, and engineering teams to identify AI transformation opportunities and translate business objectives into scalable technical solutions.
Lead architectural reviews and provide technical oversight for AI/ML projects across multiple teams and business domains.
Develop enterprise standards for RAG evaluation, prompt management, agent testing, model monitoring, AI safety, and responsible AI implementation.
Partner with Data Engineering teams to architect robust data ingestion, transformation, and orchestration pipelines using dbt, Airflow, Snowflake, and Apache Iceberg.
Drive continuous innovation by evaluating emerging AI technologies, open-source frameworks, foundation models, and industry best practices.
Leadership & Strategic Responsibilities
Serve as the organization's subject matter expert for Agentic AI, Generative AI, Enterprise AI Architecture, and Intelligent Automation.
Mentor and guide senior engineers, data scientists, AI developers, and solution architects on architecture decisions and engineering best practices.
Influence technology roadmaps and long-term platform strategy for AI, data, and analytics capabilities.
Establish AI Center of Excellence standards, reusable frameworks, and governance processes across the enterprise.
Lead proof-of-concepts, architecture assessments, technology evaluations, and vendor engagements.
Provide thought leadership on emerging AI trends, autonomous agents, foundation models, AI governance, and enterprise adoption strategies.
Technical Expertise Expected
Deep expertise in Agentic AI architectures, multi-agent collaboration frameworks, and autonomous workflow orchestration.
Strong experience building enterprise-grade RAG systems, semantic search solutions, vector databases, and knowledge retrieval platforms.
Advanced proficiency in Python, AI orchestration frameworks (LangGraph, AutoGen, LlamaIndex), and cloud AI platforms.
Extensive experience with Snowflake, Apache Iceberg, distributed data engineering, and large-scale enterprise data ecosystems.
Expertise in MLOps, CI/CD, MLflow, Kubernetes, Docker, and production AI deployment patterns.
Strong understanding of AI governance, model security, compliance, privacy, and responsible AI principles.
Experience designing highly scalable, resilient, and secure AI platforms capable of supporting enterprise-wide adoption
Actual salary is determined based on the role, location, individual experience, skills, and other considerations. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
