Developer I (AI)

• 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.

• 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

• 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).

• 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.

• 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.

• 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.

• 1-3 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.

• 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.

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