This role demands a seasoned leader to architect and deploy cutting-edge AI/ML solutions, including LLM-based systems and multi-agent workflows, while ensuring seamless integration across cloud platforms and Databricks. The ideal candidate will drive enterprise-scale AI initiatives with a focus on scalability, operational efficiency, and measurable business impact.
Experience Range: With at least 8 years of experience in AI/ML engineering, machine learning, data science, software engineering, or related fields Key Responsibilities:
Design and implement advanced machine learning and AI solutions, including LLM-based applications and agentic AI systems, to address complex business challenges and deliver measurable business outcomes
Lead the development, deployment, and operation of production AI/ML and GenAI models across major cloud platforms such as Azure, AWS, or GCP, ensuring high availability and scalability
Build, orchestrate, and optimize AI agents and autonomous workflows, focusing on robust memory, context management, and multi-agent architectures
Drive enterprise AI/ML workloads within the Databricks ecosystem, leveraging Databricks AI Agents, Model Serving, Vector Search, MLflow, and Unity Catalog to enhance operational efficiency
Establish and maintain MLOps and LLMOps practices, including CI/CD pipelines, model lifecycle management, experiment tracking, evaluation, and monitoring for continuous improvement
Develop and apply RAG architectures, embeddings, vector databases, prompt engineering, and LLM evaluation frameworks to improve model performance and reliability
Ensure AI security, responsible AI practices, data privacy, and effective mitigation of hallucination, prompt injection, and GenAI guardrails
Mentor engineers and provide technical leadership, collaborating with cross-functional teams to deliver scalable, enterprise-grade AI solutions
Required Skills:
Advanced hands-on programming experience in Python
Proficiency in SQL and experience with large-scale structured and unstructured datasets
Strong practical understanding of machine learning and AI fundamentals
Hands-on experience building and deploying LLM-based applications
Expertise in agentic AI including agent orchestration, autonomous workflows, tool/function calling, planning, task decomposition, memory, and context management
Extensive hands-on experience with Databricks AI Agents and the Databricks ecosystem
Experience with MLOps and LLMOps, including CI/CD, model lifecycle management, experiment tracking, and production deployment
Proven experience deploying and operating AI/ML or GenAI models/applications in Azure, AWS, or GCP
Expertise in RAG architectures, embeddings, vector databases/vector search, prompt engineering, and LLM evaluation
Proficiency with LLM and GenAI frameworks/orchestration tools such as LangChain, LangGraph, Semantic Kernel, or similar technologies
Preferred Skills:
Experience building enterprise-grade agentic AI platforms or multi-agent systems
Expertise with Databricks Model Serving, Vector Search, MLflow, Unity Catalog, and related Databricks AI/ML capabilities
Experience with Kubernetes, Docker, REST APIs, microservices, and CI/CD pipelines
Experience with managed GenAI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI
Experience with vector databases like Pinecone, Azure AI Search, Weaviate, or Databricks Vector Search
Experience optimizing LLM applications for latency, throughput, scalability, token consumption, and cost
Desired Qualifications:
Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, or a closely related discipline
Certification in machine learning, AI engineering, or data science from a recognized institution such as TensorFlow Developer Certificate or Databricks Certified Professional Data Scientist
Certification in cloud platforms or MLOps, for example AWS Certified Machine Learning Specialist or Azure AI Engineer Associate
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