Sr. Software Engineer

#JLLTechAmbitions

JLL, an international real estate management company, is building AI into the way its teams work with enterprise data. As an AI Engineer you will turn proofs of concept into reliable, governed, production services: agents that can call tools, remember context, respect user permissions, and be traced, evaluated, and costed.

You will work across both technology tracks as project priorities shift: agent engineering on AWS (Amazon Bedrock and AgentCore) and data and AI engineering on Oracle (AI Data Platform and AI Database). The ideal candidate is a hands-on builder with a “Get it done” attitude, strong engineering discipline, and a habit of asking the right questions before building.

Responsibilities

• Build agents on AWS: design, deploy, and operate AI agents on Amazon Bedrock AgentCore, using AgentCore Runtime for hosting, Gateway to expose APIs and Lambda functions as tools (including MCP), Memory for short- and long-term context, Identity for secure agent access, and Observability for tracing and debugging.
• Build RAG and agent solutions with Amazon Bedrock: implement foundation-model integrations, Knowledge Bases, Agents, and Guardrails, and manage prompts and model selection for cost, latency, and quality.
• Build on Oracle AI Data Platform (AIDP): develop in the AIDP Workbench using Master Catalog assets, Spark notebooks, and workflows; apply medallion (bronze, silver, gold) data patterns; and build knowledge bases and agent flows on governed enterprise data.
• Build on Oracle AI Database: implement AI Vector Search, hybrid (vector + relational) retrieval, Select AI natural-language-to-SQL interfaces, and in-database ML, with embedding pipelines and PL/SQL where needed.
• Integrate across platforms: connect Oracle data services with AWS-hosted AI applications through secure, well-defined APIs and swappable service interfaces.
• Engineer retrieval quality: own chunking and embedding strategy, metadata filtering, reranking, and retrieval tuning, and measure results with repeatable evaluations.
• Secure and govern AI: apply IAM least privilege, database-enforced access control (such as role-based access and data redaction), PII handling, and guardrails that screen for prompt injection and unsafe output. Keep audit trails of agent actions and outcomes.
• Test and evaluate: build evaluation and regression suites for LLM output, track hallucination and accuracy, and verify behavior end to end against real services, not only with unit tests.
• Deploy and operate: ship Python services (for example FastAPI) to production on AWS using Lambda, API Gateway, S3, ECS/EKS, Step Functions, and Secrets Manager. Monitor latency, quality, and cost with CloudWatch and AgentCore Observability.
• Collaborate and document: take part in sprint ceremonies and code and design reviews, and maintain documentation, runbooks, and knowledge-transfer material.
  
  

Experience & Education

• 4-8 years of software, data, or AI/ML engineering experience, with hands-on delivery of generative AI or LLM-based applications in production or near-production.
• Bachelor’s degree or higher in Computer Science, AI, or a related field is preferred.
• Proven experience building RAG pipelines and agentic workflows, including tool calling and multi-step orchestration.
• Experience with Amazon Bedrock and AWS-native deployment. Hands-on AgentCore experience is strongly preferred.
• Experience with Oracle Database or Oracle AI Database. Experience with the Oracle AI Data Platform is strongly preferred.
• Strong debugging and root-cause analysis skills, including tracing non-deterministic model behavior.
• Clear written and verbal communication, and the ability to work independently in a distributed team.
  
  

Technical Skills & Competencies

• Generative AI on AWS: Amazon Bedrock foundation models, Knowledge Bases, Agents, Guardrails, prompt engineering, and model evaluation; Amazon Bedrock AgentCore (Runtime, Gateway, Memory, Identity, Observability, and built-in tools such as Code Interpreter).
• Oracle data and AI: Oracle AI Database (AI Vector Search, Select AI, in-database ML, PL/SQL) and Oracle AI Data Platform (Master Catalog, notebooks, workflows, knowledge bases, agent flows, OCI Generative AI integration).
• AI application engineering: RAG pipelines, agentic workflows, LangChain, LangGraph, or LlamaIndex, Model Context Protocol (MCP), embedding strategy, chunking, reranking, and structured LLM output.
• AWS cloud: Lambda, API Gateway, S3, ECS/EKS, Step Functions, IAM, VPC, CloudWatch, and Secrets Manager.
• Programming: Python (primary), REST APIs, and SQL; FastAPI or similar async frameworks.
• Security and governance: IAM least privilege, data privacy controls, PII handling, and responsible-AI guardrails.
• Delivery: Agile/Scrum, Git and CI/CD, Docker, Jira and Confluence, and clear documentation.
  
  

Preferred (Nice To Have)

• Experience with AgentCore Identity and Gateway for secure, tool-enabled agents.
• Experience with Oracle Cloud Infrastructure (OCI), Apache Spark, or medallion-architecture data pipelines.
• Infrastructure as code (for example Terraform) and Kubernetes experience.
• Experience with agent frameworks such as Strands Agents, or with migrating Bedrock Agents to a code-first framework.
• Experience with LLM gateways, token and cost tracking, and usage analytics.
• Relevant certifications, such as AWS or Oracle Cloud Infrastructure Generative AI certifications.