Job DescriptionAI Engineer Overview
We are seeking an AI Engineer to help us with the development, deployment, and scaling of advanced AI applications that address real-world challenges. In this role, you will focus on designing, building, and scaling advanced AI applications in the PropTech space. You will work with state-of-the-art foundation models, RAG architectures, and multi-agent systems while partnering closely with product, design, and engineering teams.
You will be responsible for taking AI concepts from ideation to production, owning end-to-end solutions that improve our products and transform user experiences.
In this role you would be expected to come into the office 2-3 days a week (Tues & Wed are in office days). Responsibilities
- Agentic AI & Generative Application Engineering
- Evaluate and use LLMs and multimodal models from multiple providers (e.g., OpenAI, Google, Anthropic, etc.) for:
- Conversational assistants, task-based copilots, and AI agents
- Summarization, content generation, document understanding, generative analytics
- Basic multimodal use cases (text + image, text + document, and soon video/audio)
- Design and implement agentic workflows (e.g., tool-calling, multi-step reasoning, multi-agent orchestration) using:
- LangChain, OpenAI Agents SDK, Google ADK or similar frameworks.
- Prompt Engineering & Guardrails
- Design and optimize prompts and system instructions to:
- Improve task completion, reliability, and latency
- Minimize hallucinations and toxic/unsafe outputs
- Implement structured outputs (JSON/JSON Schema)
- Develop function/tool calling and prompts that help AI call them properly
- Integrate safety/guardrail layers (e.g., content moderation APIs, Guardrails AI, Rebuff, custom policies) to keep conversations focused
- RAG & Knowledge Integration
- Architect and implement RAG pipelines:
- Choose and configure vector databases (e.g., PGVector, Vertex AI Search, Pinecone, etc.)
- Build ingestion pipelines for internal data (documents, tickets, logs, property data, etc.)
- Implement knowledge retrieval process that draws from multiple sources and uses reranking to improve the response quality.
- Explore emerging retrieval techniques (semantic caching, knowledge graphs, long-context models, memory systems).
- Full-Stack & System Integration
- Build or integrate front-end experiences (React / Vue / Svelte / Web RTC) for AI agents and copilots.
- Develop back-end services to orchestrate AI calls using REST, gRPC, WebSockets, or MCP; ensure scalability and observability.
- Integrate with internal systems and PropTech data sources using secure APIs and data contracts.
- Evaluation, Monitoring & Optimization
- Design and maintain evaluation pipelines and benchmarks for LLM-based features:
- Offline metrics (accuracy, relevance, latency, cost)
- Human-in-the-loop evaluations where needed
- Use AI observability and tracing tools (e.g., LangSmith, OpenTelemetry, etc.) to monitor quality.
- Optimize for performance, reliability, latency, and cost through:
- Model selection and routing (e.g., small vs. large models, Google vs. OpenAI)
- Prompt/token optimization and caching strategies.
- Collaboration, Documentation & Delivery
- Collaborate with cross-functional teams (Product, Design, Domain Experts, Data Science, Platform Engineering) to define requirements and success metrics.
- Participate in architecture and design reviews; write clear technical documentation and runbooks.
- Contribute to shared libraries, templates, and best practices for AI development.
- Work in an Agile environment and own features from design through deployment and maintenance.
Qualifications
- 5+ years of total experience in Software Engineering and/or Data Science, with at least 2 years focused on Generative AI/LLMs.
- Degree in Computer Science, Machine Learning, Data Science, or related field, or equivalent practical experience.
- Strong proficiency in:
- Python for AI/ML, data pipelines, and back-end services
- JavaScript/TypeScript for front-end and/or Node services
- SQL and experience working with relational databases and basic data modeling
- Working with coding assistants like Windsurf, Cursor, Codex, etc.
- Proven experience building production-grade software:
- Writing clean, testable, maintainable code
- Using CI/CD pipelines, code reviews, and Git workflows
- Hands-on experience with:
- At least one agentic/orchestration framework (OpenAI Agents SDK, Google ADK, LangChain, etc.)
- LLM APIs and/or open-source models (e.g., via OpenAI, Google, Hugging Face, Ollama)
- Vector embeddings, vector databases, and RAG architectures
- Experience with one or more major cloud platforms (GCP, Azure, and AWS) and:
- Docker for containerization
- Kubernetes or a managed container service (e.g., EKS, GKE, AKS)
- Strong communication skills and ability to collaborate with both technical and non-technical stakeholders.
Nice-to-Have Skills / Abilities - Experience with:
- Voice-enabled AI agents (STT, TTS, WebRTC, Twilio Voice, Socket.IO, VAPI)
- Multimodal models (e.g., GPT models including Realtime, Gemini Pro Vision, etc.)
- Orchestrating multiple models (routing, ensembles, fallback strategies)
- Familiarity with:
- AI experiment tracking and evaluation frameworks (e.g., OpenAI Evals, Langsmith Evals, etc.)
- Feature stores, data versioning (e.g., Feast, DVC), and MLOps workflows
- Browser automation software such as PlayWright
- Background in:
- AI security, privacy, and compliance (PII handling, SOC2, GDPR considerations)
- A/B testing and online experimentation for AI features.
SALARY AND BENEFITS - Competitive salary package along with a comprehensive benefit plan that includes:
- Health, dental, and vision insurance.
- Retirement savings plan with company match.
- Paid time off and holidays.
- Professional development opportunities.
Meet Your RecruiterKhalid Vaani
