About the Role
We are seeking a skilled and driven AI Engineer to design, build, and deploy intelligent systems and AI-powered solutions that support our real estate, finance, sales, and operations functions. You will work closely with data teams, business stakeholders, and enterprise IT to develop scalable, secure, and production-ready AI applications that deliver measurable business value.
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Key Responsibilities
- Design, develop, and deploy machine learning and generative AI models and pipelines to solve business problems across departments (finance, sales, operations, customer experience).
- Build and maintain AI-powered automation tools, including LLM-based applications, chatbots, and document/data processing systems.
- Collaborate with business units to identify opportunities where AI/ML can improve efficiency, decision-making, or customer engagement.
- Integrate AI models with enterprise systems (ERP, CRM, data warehouses) following approved architecture and security standards.
- Develop and maintain data pipelines to support model training, evaluation, and monitoring.
- Ensure all AI solutions comply with data privacy, security, and governance requirements, working with IT Security and Compliance as needed.
- Conduct model evaluation, testing, and validation to ensure accuracy, fairness, and reliability before production deployment.
- Document technical designs, model architectures, and deployment processes for auditability and knowledge transfer.
- Stay current with advances in AI/ML (including LLMs, RAG architectures, and agentic systems) and recommend enterprise-appropriate adoption.
- Support change management by testing solutions in Dev/UAT environments prior to production rollout, with rollback and monitoring plans in place.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
- 3+ years of experience in AI/ML engineering, data science, or software engineering with a focus on applied AI.
- Strong programming skills in Python; familiarity with ML frameworks (PyTorch, TensorFlow, scikit-learn).
- Experience with LLM integration (e.g., via Anthropic, OpenAI, or similar APIs), prompt engineering, and retrieval-augmented generation (RAG).
- Solid understanding of data structures, algorithms, and software engineering best practices.
- Experience with cloud platforms (Azure, AWS, or GCP) and MLOps tools for model deployment and monitoring.
- Familiarity with enterprise data governance, security, and compliance considerations for AI systems.
- Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
- Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders.
