Principal AI Engineer

Duties & Responsibilities Architect and oversee end-to-end AI/ML platforms, including GenAI, LLMs, agentic workflows, and MLOps/LLMOps infrastructure. Lead the design and deployment of enterprise-grade GenAI applications, integrating advanced models (e.g., GPT-4, LLaMA, multimodal AI) for complex business use cases. Set technical standards for model selection, fine-tuning, evaluation, and responsible AI governance across teams. Drive adoption of best practices in data engineering, feature pipelines, and production AI using Databricks, Azure AI, Snowflake, Spark, Airflow, and related technologies. Champion Human-in-the-Loop (HITL) validation, feedback loops, and continuous improvement for production AI systems. Mentor and coach Lead/Staff AI Engineers, fostering technical excellence and career growth. Collaborate with cross-functional leaders (Product, Data, Engineering, Business) to define AI strategy, roadmap, and KPIs. Oversee AI observability, monitoring, and guardrail management using Unity Catalog, Azure AI Guardrails, OpenTelemetry, and similar tools. Represent the organization in external forums, conferences, and vendor engagements as a thought leader in AI/ML.

Requirements Basic Qualifications Experience working in agile, cross-functional teams and delivering measurable impact. Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field. 11+ years as over all work experience in IT industry and 8+ years of experience architecting and deploying enterprise-scale AI/ML platforms, including GenAI, LLMs, agentic architectures, and MLOps/LLMOps. Proven expertise in designing, implementing, and scaling AI infrastructure using Databricks, Azure AI, Snowflake, Spark, Airflow, and related technologies. Hands-on experience with production-grade model selection, fine-tuning, evaluation, and responsible AI governance. Strong programming skills in Python, PySpark, SQL, and experience with modern AI/ML frameworks (e.g., LangChain, Hugging Face, OpenAI Agents SDK). Demonstrated ability to lead cross-functional teams and deliver measurable business impact through AI-driven solutions. Experience integrating GenAI models into enterprise applications with Human-in-the-Loop (HITL) validation and feedback loops. Familiarity with multi-modal AI (text, image, audio) and unstructured data processing. Preferred Qualifications Experience architecting and operating large-scale AI platforms in cloud environments (Databricks, Azure AI Foundry, Vertex AI). Deep knowledge of agentic frameworks (LangGraph, Databricks Genie, Azure AI Agent Orchestration) and orchestration of autonomous AI workflows. Advanced proficiency in MLOps/LLMOps/AgentOps tools: MLflow, ONNX, DVC, Unity Catalog, CI/CD pipelines. Expertise in data engineering: DBT, Spark, Lakehouse, Azure Data Factory, Snowflake. Strong background in observability, governance, and guardrail management: OpenTelemetry, Databricks AI Gateway, Azure Guardrails. Publications, patents, or conference presentations in AI/ML or platform architecture. Experience with regulatory compliance, responsible AI practices, and enterprise security. Proven track record of mentoring and developing technical talent. Experience with global teams and multi-region deployments.