Senior AI Engineer

Location

Business - Real Estate

Brookfield Real Estate Group is one ofBrookfield’s primary operating groups. The Real Estate Group is one of the world's largest investors in real estate, with a global portfolio that includes office, retail, multifamily, logistics, hospitality and alternative real estate assets on five continents. The Group owns and operates approximately $268 billion of assets representing the most iconic properties in the world’s most dynamic markets. We seek to generate value by leveraging our operating expertise and focusing on our core real estate capabilities of leasing, financing, development, design and construction as well as property and facilities management. For more information, visit .

Brookfield Culture

Brookfield has a unique and dynamic culture. We seek team members who have a long-term focus and whose values align with our Attributes of a Brookfield Leader: Entrepreneurial, Collaborative and Disciplined. Brookfield is committed to the development of our people through challenging work assignments and exposure to diverse businesses.

Job Description

Brookfield is seeking a Senior ML/AI Engineer to help build and scale an AI-first platform that amplifies Brookfield's intelligence and enables smarter investment decisions, faster.

This role is for a hands-on engineer who will design, build, and improve the ML and AI application systems that turn Brookfield's proprietary investment context, including deals, documents, research, decisions, and outcomes, into reusable institutional intelligence.

You will work across retrieval, document intelligence, knowledge graphs, and LLM-powered applications, supporting capabilities ranging from natural-language query over structured data to grounded Q&A across large virtual data rooms, cross-corpus search, and agentic workflows.

You will partner closely with Product, Engineering, and Investment teams to deliver ML/AI capabilities that are accurate, traceable, scalable, and trustworthy enough to support real investment decisions. The ideal candidate combines strong hands-on ML/AI engineering experience with product judgment, curiosity, and a pragmatic approach to solving complex problems.

RESPONSIBILITIES

• Design, build, and maintain ML/AI systems across knowledge graph intelligence, document intelligence, retrieval, and LLM-powered applications.
• Build retrieval capabilities that combine structured data, unstructured documents, embeddings, metadata, and graph-based relationships.
• Contribute to the development of knowledge graph capabilities that support natural-language query, entity linking, and context assembly.
• Build and improve document processing pipelines for large-scale virtual data rooms, including ingestion, OCR, layout-aware parsing, table extraction, structured fact extraction, and citation-grounded Q&A.
• Develop hybrid retrieval and reranking systems combining lexical, dense, metadata, and graph-based approaches across large datasets.
• Build LLM applications using RAG, agent and workflow orchestration, prompt and tool design, structured outputs, and grounding techniques.
• Develop and maintain evaluation frameworks, including golden sets, automated evaluations, human validation workflows, online metrics, and regression testing.
• Evaluate foundation models, embedding models, vector stores, and document AI services based on quality, latency, cost, and product requirements.
• Build cost-efficient ML/AI systems using techniques such as caching, model cascading, tiered processing, and appropriate batch versus real-time processing.
• Implement permissions-aware retrieval and AI pipelines that respect data access controls and audit requirements.
• Ensure AI-generated outputs are grounded in and traceable to source evidence.
• Partner with Full Stack, Platform, Product, and other AI Engineers to integrate shared ML/AI capabilities into products and workflows.
• Contribute to model fine-tuning and adaptation initiatives where off-the-shelf models do not meet product requirements.
• Support other engineers through code reviews, technical discussions, and shared engineering standards.
• Improve retrieval, ranking, generation, and recommendation quality using user feedback, validated outputs, and other product signals.
• Stay current with developments across LLMs, retrieval, document intelligence, agentic systems, and AI engineering, applying new approaches where they meaningfully improve the platform.

QUALIFICATIONS

SERVICE / PERSONAL SKILLS

• Strong systems thinker with the ability to design practical ML/AI solutions for complex product and data problems.
• Clear communicator who can explain ML/AI tradeoffs and model behavior to both technical and non-technical stakeholders.
• Highly collaborative and comfortable working across Product, Engineering, Data, and Investment teams.
• Execution-focused and pragmatic, with good judgment around build-versus-buy decisions.
• Passionate about AI and interested in applying it to high-stakes, judgment-heavy domains where accuracy and trust are critical.
• High-agency builder who is comfortable with ambiguity and enjoys getting close to users and their workflows.

EXPERIENCE

• 5+ years of professional experience in machine learning, applied AI, software engineering with an ML focus, or ML/AI systems engineering, with experience shipping production systems.
• Strong experience with retrieval systems, including embeddings, vector search, hybrid retrieval, and reranking.
• Experience developing LLM applications using RAG, agent or workflow orchestration, prompt and tool design, structured outputs, and grounding.
• Experience with knowledge graphs or relationship-heavy retrieval, including areas such as entity resolution, graph traversal, or graph-aware context assembly.
• Experience with document intelligence pipelines, including OCR, parsing, table extraction, and citation-preserving chunking.
• Experience developing and applying evaluation approaches for ML/AI systems, including automated evaluation, golden datasets, human validation, and regression testing.
• Understanding of permissions-aware AI systems and access control within retrieval and generation pipelines.
• Strong proficiency in Python and familiarity with modern ML/AI development frameworks and tooling.
• Experience with cloud-native ML/AI infrastructure; experience with AWS, FastAPI, or TypeScript is an asset.
• Understanding of the cost, latency, and quality tradeoffs associated with foundation models and AI infrastructure.
• Experience with model fine-tuning, training, or adaptation is an asset.
• Experience in data-rich, judgment-heavy domains such as real estate, finance, asset management, or legal is an asset.
• Comfortable using AI coding tools and agents to improve engineering productivity.
• Able to assess AI-generated code critically, understanding when outputs can be trusted and when deeper review is required.
• Interested in improving AI engineering workflows, tooling, guardrails, and feedback loops over time.

EDUCATION / DESIGNATIONS / LICENSES

• Bachelor's degree in Computer Science, Machine Learning, Statistics, or a related technical field required, or equivalent practical experience.
• Advanced degree (MS or PhD) in a quantitative or ML/AI-related field is preferred.
• Cloud certifications, including AWS, Azure, or GCP architecture or ML/AI certifications, are considered an asset.

Brookfield is committed to accommodating the individual needs of all applicants with disabilities during the recruitment and selection process in accordance with applicable laws.

Our compensation structure is comprised of a base salary and a short-term incentive program (cash bonus). Cash compensation tends to vary based on geography to account for local market conditions and is set to be market competitive. Compensation decisions are based on a number of factors including relative experience, overall years of experience, industry experience, education and designations.

Brookfield is committed to maintaining a Positive Work Environment that is safe and respectful; our shared success depends on it. We do not tolerate workplace discrimination, violence or harassment. We are proud to be an Equal Opportunity Employer and make employment decisions based on qualifications, merit and business needs, without regard to any characteristic protected by applicable law. Applicant information is collected and handled in accordance with our Applicant Privacy Notice.

Originally posted on Himalayas