Staff Data Engineer

Job Overview As a Forward Deployed Data Engineer, you will embed with product, program, and engineering teams to rapidly design, architect, prototype, and deploy data-driven solutions that solve high-priority business problems. You will move from concept to prototype in days, maintaining enterprise-grade standards for quality, security, and scalability. Key Responsibilities Lead solution design for complex, cross-functional data and AI problems, from discovery through technical blueprint. Define and communicate architecture decisions, trade-offs, and delivery approaches to technical and non-technical audiences. Design scalable, modular systems balancing speed with enterprise standards for reliability, security, and maintainability. Participate in architecture reviews and Critical Design Reviews (CDRs) to ensure alignment with enterprise patterns and platform standards. Create clear technical documentation: architecture diagrams, data flow maps, API contracts, and solution briefs. Design and deliver working prototypes for complex data and AI problems within compressed timeframes. Balance speed of delivery with enterprise standards; prototypes are production-ready. Continuously iterate on solutions based on feedback from product managers, program leads, and end users. Design, build, and deploy AI agents and multi-agent systems that automate complex workflows end-to-end. Develop and maintain agent skills—discrete, reusable capabilities that compose into larger agentic pipelines. Implement and extend MCP (Model Context Protocol) servers and clients to connect AI agents with enterprise tools, APIs, and data sources. Build agent orchestration layers using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel. Design evaluation harnesses, guardrails, and monitoring pipelines to ensure agent reliability and safety in production. Build and deploy AI-powered features and pipelines that automate workflows, surface insights, and enhance decision-making. Design and implement scalable data pipelines, APIs, and backend services that serve internal tools and customer-facing products. Integrate LLMs, RAG systems, and ML models into production data workflows. Own data modeling, transformation, and quality across the solutions you deliver. Embed directly with product, program, and engineering teams to co-define problems and co-deliver solutions. Influence technical direction and build alignment across teams without formal authority. Communicate complex technical concepts clearly to non-technical business stakeholders in writing, meetings, and executive presentations. Mentor and elevate junior engineers, sharing patterns and practices for agentic development, prompt design, and rapid delivery. Foster a collaborative, low-ego team culture where speed and quality go hand in hand. Qualifications Required 6+ years of professional software or data engineering experience, including solution design and architecture ownership. Demonstrated ability to architect end-to-end data and AI systems—clear documentation and stakeholder communication. Hands-on experience building AI agents, including defining agent skills, tool use, memory, and multi-step reasoning. Working knowledge of MCP (Model Context Protocol) to build or consume MCP servers connecting agents with external systems. Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel. Strong hands-on experience with data engineering: pipelines, ETL/ELT, data modeling, SQL and NoSQL databases. Proficiency in Python. Experience with cloud platforms (AWS, Azure, or GCP) and modern data stack tooling. Exceptional communication and interpersonal skills; earn trust quickly, navigate ambiguity, and drive alignment. Comfort in fast-paced environments with shifting priorities and high ownership expectations. Preferred Experience with RAG architectures, vector databases (Pinecone, Weaviate, pgvector), semantic search, and building indexing systems and data processing pipelines. Familiarity with prompt engineering, fine-tuning, and LLM evaluation techniques. Experience with agent observability and tracing tools (LangSmith, Arize, Weights & Biases, or similar). Experience with AWS AgentCore—building, deploying, and operating agents on the platform. Knowledge of data orchestration tools (Airflow, Prefect, or dbt). Experience with containerization and CI/CD practices (Docker, Kubernetes, GitHub Actions). Background in real estate, financial services, or other data-intensive enterprise domains. Experience facilitating technical discovery workshops, design sprints, or architecture reviews. Contributions to open-source agentic or data projects, or a portfolio demonstrating rapid, creative problem-solving. This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without sponsorship. Estimated Compensation 160,000.00 - 200,000.00 USD per year Location On-site – Chicago, IL, Westmont, IL Benefits 401(k) plan with matching company contributions Comprehensive Medical, Dental & Vision Care Paid parental leave at 100% of salary Paid Time Off and Company Holidays Early access to earned wages through Daily Pay Equal Employment Opportunity Jones Lang LaSalle (“JLL”) is an Equal Opportunity Employer and is committed to working with and providing reasonable accommodations to individuals with disabilities. #J-18808-Ljbffr