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Remote role but highly specialized Agentic AI skills and senior requirement balance to medium competition.
Specialized ML/AI, knowledge-graph, and Databricks expertise reduce cross-industry transferability, so sensitivity is high.
Explicit 8+ years plus mandatory Agentic AI, Databricks, knowledge-graph, and LLM requirements make filters high.
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Design, develop, and deploy Agentic AI applications using LLMs and multi-agent frameworks with measurable impact on scalability and integration.
Build scalable AI solutions leveraging Databricks, vector databases, knowledge graphs, RAG pipelines, and AI orchestration workflows.
Optimize AI models focusing on performance, scalability, security, and governance, ensuring seamless integration with enterprise applications and APIs.
8+ years of software engineering experience with languages such as Java, Python, or Scala.
2–3 years of hands-on experience specifically in Agentic AI or Generative AI.
Strong expertise in Databricks, Spark, ML workflows, and Knowledge Graph implementations (e.g., Neo4j, GraphFrames).
Experience with cloud platforms (Azure, AWS, GCP) and MLOps/CI-CD practices.
Experienced in building and deploying complex Agentic AI systems with operational knowledge of RAG pipelines, vector databases, and AI orchestration frameworks (LangGraph, LangChain, AutoGen, CrewAI).
Skilled in integrating AI solutions within enterprise environments requiring optimization for scalability, security, and governance.
Technically proficient in both AI and software engineering disciplines with a strategic focus on AI model orchestration, knowledge graphs, and cloud-based AI deployments.