Senior Software Engineer

Job description

JLL empowers you to shape a brighter way.   Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong.  Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward.    About the Role We are seeking an experienced Senior Software Engineer specializing in Agentic AI to join our Innovation engineering team at JLL Technologies. You will design, build, and deploy production-grade multi-agent AI systems that power next-generation intelligent features within Azara, our AI-driven data intelligence platform for commercial real estate. This role sits at the intersection of software engineering and applied AI, requiring you to architect autonomous agent workflows, build RAG pipelines, orchestrate LLM interactions, and deliver AI solutions that create tangible business value at enterprise scale. Key Responsibilities Agentic AI Architecture & Development • Design and build production-grade multi-agent systems using LangGraph as the primary orchestration framework, with knowledge of LangChain, CrewAI, and AutoGen • Architect agent orchestration patterns including planning, tool use, persistent state management, memory, reflection, and multi-agent coordination • Develop and optimize RAG (Retrieval-Augmented Generation) pipelines with document processing, chunking strategies, embedding workflows, and vector database integration • Build robust agent evaluation, testing, and observability frameworks to ensure reliability and performance in production • Design natural language to data query solutions integrating with platforms such as Databricks Genie LLM Integration & Optimization • Integrate and manage LLM/SLM services (OpenAI, Azure OpenAI, Anthropic, open-source models) with appropriate model selection, prompt engineering, and cost optimization • Design prompt engineering strategies including chain-of-thought, few-shot, and structured output techniques for reliable agent behavior • Implement guardrails, safety mechanisms, and content filtering for AI-generated outputs • Evaluate and benchmark models for latency, accuracy, cost, and domain-specific performance Platform & Backend Engineering • Build scalable Python backend services (FastAPI) that serve AI agent workflows to production applications at enterprise scale • Design and implement caching, rate limiting, persistent agent state, and conversation memory strategies • Develop event-driven microservices and real-time streaming for AI agent interactions • Develop APIs and integration layers that connect AI agents with enterprise data sources, tools, and external services • Implement distributed task processing (Celery) and event-driven autoscaling (KEDA) for production AI workloads Innovation & Technical Leadership • Stay current with the rapidly evolving Agentic AI landscape and evaluate emerging frameworks, models, and techniques • Lead proof-of-concept development for new AI capabilities, moving successful experiments to production • Mentor engineers on AI engineering best practices, prompt engineering, and agent design patterns • Contribute to technical documentation, architecture decision records, and AI solution design specifications • Champion the adoption of AI-powered development tools (Cursor AI, GitHub Copilot) across engineering teams Required Qualifications • Strong proficiency in Python with hands-on experience building production AI applications • Demonstrated experience with LangGraph or similar agentic AI frameworks (LangChain, CrewAI, AutoGen) for production systems • Hands-on experience with LLM API integration (OpenAI, Azure OpenAI, Anthropic) and prompt engineering • Experience designing and implementing RAG systems including embedding models, vector databases, and retrieval strategies • Solid understanding of multi-agent system design, agent orchestration, persistent state management, and memory patterns • Experience with Python web frameworks (FastAPI) and distributed task processing (Celery) for production APIs • Experience with event-driven microservices (Dapr) and real-time streaming patterns (SSE) • Proficiency with AI-powered development tools (Cursor AI, GitHub Copilot, or similar) for AI-augmented software development across the SDLC • Proficiency with Git, CI/CD pipelines, and cloud platforms (preferably Azure) Preferred Qualifications • Experience with vector databases (Qdrant, Pinecone, PgVector, ChromaDB) • Experience with Databricks Genie or similar natural language to data query platforms • Experience with AWS Bedrock AgentCore for managed agent runtime and multi-cloud agent deployment • Experience with multi-tenant architecture patterns and enterprise-scale AI systems • Experience with containerization (Docker, Kubernetes) and event-driven autoscaling (KEDA) • Understanding of AI safety, responsible AI principles, and enterprise governance requirements <p