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Posted 4 months ago
Requirements and responsibilities
Requirements:Bachelors or Masters degree in Computer Science, Software Engineering, AI/ML, Data Science, or related field (or equivalent experience).5-8 years of professional experience in AI/ML engineering, Data Engineering, or Applied AI development.Solid foundation in Python with experience building scalable AI/ML applications.Hands-on experience with agentic toolkits, including Google Agentic/Vertex AI Agent Builder and the Aera Agentic Platform (or similar autonomous decisioning platforms).Practical exposure to open-source agentic frameworks, such as LangChain, AutoGen, CrewAI, and the HuggingFace stack.Experience deploying LLMs, RAG pipelines, vector databases (e.g., Pinecone, Weaviate, Chroma, BigQuery Vector search).Good understanding of MLOps and LLMOps: CI/CD, model versioning, experiment tracking, and monitoring.Familiarity with cloud ecosystems (GCP preferred; AWS/Azure as a plus).Strong problem-solving and system design skills with ability to work in fast-paced environments.Experience building autonomous decisioning platforms for manufacturing, finance, supply chain, or enterprise automation.Exposure to streaming data systems (Kafka, Kinesis, Pub/Sub).Knowledge of Graph-based reasoning and enterprise knowledge management.Experience working with API-driven enterprise platforms (SAP, Salesforce, ServiceNow, etc.).Excellent communication and documentation abilities.Strong ownership mindset and ability to work independently with minimal supervision.Ability to collaborate with cross-functional technical and business teams.Responsibilities:Design, architect, and develop agentic AI pipelines and multi-agent systems for data ingestion, processing, and analytics.Build and optimize Lakehouse-based data solutions on Databricks including ETL/ELT pipelines, Delta Lake storage, and ML model operationalization.Implement and orchestrate AI Agents using tools such as Google Vertex AI Agent Builder, Aera/AREA agents, or equivalent agentic frameworks.Integrate LLMs and foundation models into data workflows for autonomous decisioning and self-service insights.Develop, test, and deploy RAG (Retrieval Augmented Generation) and LLMOps workflows to support domain-specific knowledge reasoning.Evaluate and implement best practices for prompt engineering, fine-tuning, guardrails, and responsible AI in production.Collaborate with data engineers, MLOps, and product teams to ensure scalability, resilience, and security of the agentic platform.Monitoring and performance optimization of deployed agents and AI workflows.
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