LOCAL CANDIDATES ONLY – HYBRID ROLES
Looking for AI Engineers - Agentic AI team – FULL TIME PERM ROLES
Hybrid Roles – 3 days Onsite in Richardson Texas 75082
Salary Range: $150,000 - $160,000/YR
If interested, please email your resume to [email protected]
NO C2C/1099 OR OPT
Role:
Our Agentic AI team is focused on driving innovation by building next generation AI applications and enhancing existing systems with Generative AI capabilities within the Property Tech domain.
We are seeking AI EngineeSr 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.
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.
Required:
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.