As discussed, sharing data engineering requirement for Bangalore location. Kindly share relevant profiles.
Must Have skillsets – Azure databricks, Data factory, Python, Pyspark, SQL
Experience – 5 to 8 years
Responsibilities
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
About the Role
JLL's Portfolio Services Technology function is building Navigator, a next-generation AI-powered platform that enables clients and JLL teams to model complex real estate portfolio scenarios, evaluate strategic options and make data-driven decisions at scale. Central to Navigator's intelligence layer is the ability to understand and reason about location — integrating geospatial data, demographic intelligence, labour market analytics and real estate market signals into the AI agents that power the platform.
We are looking for a Technical Product Lead — Data Scientist who combines deep data science capability with a product mindset and specialist expertise in location intelligence and geospatial AI. This is a hands-on senior role that sits at the intersection of data science, AI agent development and product strategy. You will build and own the models, pipelines and geospatial frameworks that underpin Navigator's analytical capabilities, while working directly with engineering, product and design teams to ensure those capabilities are embedded effectively into the platform.
This is not a pure research role. You will be responsible for production-grade outputs that directly inform client-facing AI recommendations — from site selection and labour force mapping to cost modelling and ESG scoring.
What You Will Do
Location Intelligence & Geospatial AI
• Design and build geospatial models that power Navigator's location scoring, market positioning and demographic analysis capabilities, integrating data from CoStar, labour market sources and third-party geospatial providers
• Develop location-based AI agent capabilities, including the Analytics Agent's real-time market intelligence layer, enabling the platform to surface relevant location insights for any given portfolio scenario
• Build and maintain geospatial data pipelines that ingest, transform and serve location data at the scale required for multi-portfolio analysis — including property-level data, demographic overlays, transport networks and ESG indicators
• Integrate map functionality into the Navigator platform, including synchronised list and map views, property clustering, search and filtering, and data overlays — working closely with engineering to ensure performance and usability
• Evaluate and onboard geospatial data sources, including CoStar and other third-party providers, assessing data quality, coverage and commercial terms for inclusion in the platform
AI Agent Development & Data Science
• Design, train and validate machine learning models that underpin Navigator's multi-criteria scoring framework — including Cost Optimisation, ESG/Sustainability, Talent Accessibility, Market Positioning and Risk Mitigation axes
• Own the data science architecture for the AI Scenario Agent, developing the model logic that generates and evaluates alternate portfolio strategies based on client-defined criteria and weighted priorities
• Build RAG pipelines and document intelligence capabilities that enable agents to reason over lease documents, market reports and financial data — directly relevant to the Lease Admin Agents programme
• Define evaluation frameworks and guardrails for AI output quality, including bias detection, accuracy validation and responsible AI compliance — ensuring model outputs are commercially credible and production-ready
• Monitor and optimise model performance in production, using NPS data from Pendo and user feedback from Optimal Workshop to identify where model outputs are falling short and iterating accordingly
Product Strategy & Stakeholder Collaboration
• Act as the data science product authority, translating business requirements from deep dive workshops and SME sessions into data science specifications that engineering teams can build against
• Own the analytical product roadmap, defining how location intelligence, geospatial AI and data science capabilities will evolve across Navigator's development phases — from current MVP through to future iterations
• Contribute to the AWS platform selection decision, specifically assessing the geospatial and ML infrastructure capabilities of AWS AgentCore and related services against Navigator's analytical requirements
• Participate in Critical Design Reviews (CDR), providing data science and geospatial expertise to ensure architectural decisions support the analytical capabilities the product requires
• Present model outputs, methodology and validation findings to senior stakeholders and client-facing audiences in clear, non-technical language
Governance & Standards
• Maintain documentation on model methodology, data sources, validation outcomes and geospatial data lineage in Confluence — supporting CDR compliance and responsible AI governance
• Ensure all geospatial and AI models comply with JLL's data governance policies, applicable data privacy regulations, and the AI Terms Addendum obligations under vendor contracts
• Support token cost management and optimisation across AI agent interactions, working with engineering to ensure the analytical layer operates efficiently at scale
What We Are Looking For
Essential
• 10+ years of hands-on data science experience, with at least 2 years working with geospatial data, location intelligence or spatial analytics in a product or platform environment
• Strong Python proficiency, including geospatial libraries such as GeoPandas, Shapely, Folium, PostGIS or equivalent
• Demonstrated experience building and deploying AI agents or LLM-based systems in production, including RAG design, agent orchestration and output evaluation
• Experience designing multi-criteria scoring and weighting frameworks for complex decision support applications
• Proficiency building and managing data pipelines on cloud infrastructure, ideally AWS or Azure
• Experience working with commercial real estate, demographic or labour market data sources — including CoStar, CBRE, or equivalent
• Strong product mindset — able to translate data science outputs into user-facing value and communicate model behaviour to non-technical stakeholders clearly
• Ability to operate across the full delivery cycle — from requirements and architecture through to production validation and iteration
Desirable
• Experience with AWS geospatial services, SageMaker or AWS AgentCore for production ML deployment
• Familiarity with ESG data frameworks, carbon emissions modelling or sustainability scoring methodologies
• Experience in real estate, financial services or enterprise SaaS environments
• Knowledge of vector databases, embeddings and semantic search for geospatial or document intelligence use cases
• Experience with Pendo, Optimal Workshop or equivalent product analytics and user research tools
• Familiarity with map rendering libraries and spatial visualisation tools for web applications
• Knowledge of responsible AI frameworks, bias evaluation and model governance for enterprise deployment
Key Technologies
Data Science
Geospatial
AI & Agents
Platform
Python · Pandas · NumPy · Scikit-learn · SQL (Postgres / SQLite)
GeoPandas · Shapely · PostGIS · Folium · CoStar API
LangChain / LlamaIndex · RAG · Vector DBs · AWS AgentCore · Claude / OpenAI
AWS / Azure · Confluence · Jira · Pendo · Optimal Workshop
Why This Role
Location intelligence is the foundation of every meaningful portfolio decision — and no one in the real estate industry is yet using it at the level Navigator is designed to operate. This role places you at the leading edge of how geospatial AI, location data and multi-agent systems come together in a production platform used by JLL's largest clients. You will not be running experiments — you will be building the analytical engine that powers decisions worth billions in real estate value. If you have the technical depth, the product instinct and the geospatial expertise, this is the role that lets you put all three to work.
About JLL
Jones Lang LaSalle (JLL) is a leading professional services firm specializing in real estate and investment management. Operating across more than 80 countries, JLL is committed to delivering technology-led solutions that create long-term value for clients, employees and communities
Responsibilities
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
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
Technical Skills & Competencies
AI & Agentic Systems
• Primary Framework: LangGraph (multi-agent orchestration with persistent state)
• Additional Frameworks: LangChain, CrewAI, AutoGen
• LLM Providers: OpenAI (GPT-5.x), Azure OpenAI, Anthropic (Claude), enterprise LLM services
• Techniques: RAG, prompt engineering, chain-of-thought, function calling, structured outputs
• Data Intelligence: Databricks Genie (natural language to SQL)
• Vector Databases: Qdrant, Pinecone, Weaviate, ChromaDB
• Multi-Cloud: AWS Bedrock AgentCore (managed agent runtime)
• Patterns: Multi-agent orchestration, tool use, persistent state, memory management, agent evaluation
Core Engineering
• Languages: Python (primary), SQL
• Frameworks: FastAPI, Celery, Pydantic
• Databases: PostgreSQL (multi-tenant), Redis (caching, rate limiting)
• Event-Driven: Dapr, SSE (real-time streaming)
• Patterns: Microservices, event-driven architecture, distributed task processing
Experience & Education
• Bachelor's degree in Computer Science, Engineering, AI/ML, or a related technical field, or equivalent professional experience
• 6+ years of proven software engineering experience with significant hands-on AI/ML work in enterprise environments
• Strong communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders
• Strong knowledge of Agile methodologies and principles
• Demonstrated passion for staying current with the rapidly evolving AI landscape
Responsibilities
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
