AI/ML Engineer
Actively Hiring
Full-time Posted about 2 months ago
Responsibilities
- check_circle Design, build, and deploy machine learning models for real estate use cases - lead scoring, demand forecasting, churn detection and organic growth
- check_circle Develop and fine-tune Large Language Models (LLMs) for property FAQ, document processing and customer communication
- check_circle Build AI agents that automate CRM workflows, follow-ups and post-sales processes end-to-end
- check_circle Implement Retrieval-Augmented Generation (RAG) pipelines over property listings, legal documents, and market reports
- check_circle Work with structured and unstructured real estate data — transaction records, listing databases, CRM and market feeds
- check_circle Build and maintain feature engineering pipelines: conduct exploratory data analysis and model evaluation
- check_circle Collaborate with data engineers to ensure clean, reliable data flows into model training and inference
- check_circle Manage the full ML lifecycle: experimentation, versioning, deployment, monitoring, and retraining
- check_circle Build and maintain automation pipelines using workflow tools such as n8n, Zapier or custom orchestration frameworks
- check_circle Ensure models are deployed reliably with observability, alerting, and rollback mechanisms in place
- check_circle Partner with product managers, operations and CRM teams to translate business problems into AI solutions
- check_circle Stay up to date on AI/ML research and evaluate emerging tools, models and frameworks relevant to Property Tech
- check_circle Document architectures, experiments and deployment processes clearly for cross-functional teams
Basic qualifications
- 5+ years of hands-on experience building and shipping ML models in production
- Strong foundations in supervised, unsupervised and reinforcement learning
- Experience working with LLMs - prompt engineering, fine-tuning and API integration (OpenAI, Anthropic etc.)
- Familiarity with AI agent frameworks such as LangChain, LlamaIndex, CrewAI or AutoGen
- Experience with cloud platforms: AWS (SageMaker, Lambda, S3), GCP (Vertex AI) or Azure ML
- Proficiency in MLOps tools: MLflow, Weights & Biases or similar for experiment tracking and model versioning
- Comfort with containerisation (Docker, Kubernetes) and CI/CD pipelines for ML workloads
- Workflow automation experience with tools such as n8n, Apache Airflow etc.
- Strong SQL skills: experience with data warehouses (BigQuery, Snowflake or Redshift)
- Familiarity with data pipeline tools (dbt, Spark or Pandas at scale)
- Ability to work with APIs and integrate ML outputs into CRM
Preferred qualifications
- 3+ years of hands-on experience building and shipping ML models in production
- Strong foundations in supervised, unsupervised, and reinforcement learning
- Experience working with LLMs — prompt engineering, fine-tuning, and API integration (OpenAI, Anthropic, Mistral, etc.)
- Familiarity with AI agent frameworks such as LangChain, LlamaIndex, CrewAI, or AutoGen
- Experience with cloud platforms: AWS (SageMaker, Lambda, S3), GCP (Vertex AI), or Azure ML
- Proficiency in MLOps tools: MLflow, DVC, Weights & Biases, or similar for experiment tracking and model versioning
- Comfort with containerisation (Docker, Kubernetes) and CI/CD pipelines for ML workloads
- Workflow automation experience with tools such as n8n, Apache Airflow, or Prefect
- Strong SQL skills; experience with data warehouses (BigQuery, Snowflake, or Redshift)
- Familiarity with data pipeline tools (dbt, Spark, or Pandas at scale)
- Ability to work with APIs and integrate ML outputs into CRM, ERP, or product systems
- Experience with text-to-speech / speech-to-text models (ElevenLabs, Whisper etc.)
- Building voice AI agents or voice-enabled CRM integrations
- Multimodal model experience (vision + text for property image analysis)
- Hands-on with vector databases and Building production RAG pipelines
Tags & Focus Areas
Ai Ai Engineer Machine Learning Data Science Mlops Data Engineer Generative Ai