Data Science Lead
Brillio
Data Science Lead
Job requirements Experience Range: With at least 8 years of experience in AI/ML engineering, machine learning, data science, software engineering, or related fields Key Responsibilities:
Job requirements 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
- 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
- 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
- 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
Vacancy posted 6 days ago
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