Hybrid
Full time
AI/ML Engineer
Build innovative blockchain and AI applications at LabsDAO. Develop end-to-end solutions that push the boundaries of decentralized technologies.
Job description
We’re building Ink'd, an AI-powered real estate assistant designed to simplify contracts and automate complex workflows. To power our conversational AI at scale, we need strong backend systems and reliable ML pipelines.
We’re hiring a Backend / MLOps Engineer to own infrastructure, APIs, and AI model deployment; ensuring our systems are fast, reliable, and production-ready.
What You’ll Do
- Build and maintain scalable backend APIs in Python (FastAPI/Django/Flask).
- Manage databases (PostgreSQL, SQL optimization, ORMs).
- Deploy and monitor services on AWS (ECS/EKS, RDS, Lambda).
- Implement MLOps pipelines: model deployment, experiment tracking, evaluation, monitoring.
- Collaborate with the frontend and QA teams to deliver a smooth AI-driven product experience.
- Work with LangGraph, LangChain, LiveKit, and vector databases for agentic AI systems.
What We’re Looking For
- 5+ years of experience in backend or MLOps roles.
- Strong in Python and backend frameworks.
- Hands-on with Docker, Kubernetes, Terraform.
- Experience with model deployment and monitoring (SageMaker, TorchServe, BentoML).
- Clear communication and ownership mindset.
Bonus Points
- Experience with event-driven architectures (Kafka, RabbitMQ).
- Familiarity with real estate APIs (MLS/RETS) or contract generation systems.
- Background in cost optimization for LLMs.
Job requirements
Core Technical Requirements
- Strong in Python (FastAPI, Django, Flask).
- Experience with PostgreSQL, SQL optimization, and ORMs (SQLAlchemy, Prisma, TypeORM).
- Experience deploying scalable backends on AWS (ECS/EKS, Lambda, RDS, S3).
- Experience with infrastructure-as-code (Terraform, CloudFormation).
- Familiarity with containerization (Docker, Kubernetes).
- MLOps expertise:
- Model deployment (ONNX, TorchServe, SageMaker, BentoML).
- Experiment tracking (MLflow, Weights & Biases).
- Monitoring and evaluation of LLMs (latency, drift detection, hallucinations).
- CI/CD for ML workflows (GitHub Actions, Airflow, Argo).
- AWS Solutions Architect Certification
AI/Agent Systems
- Experience with LangGraph, LangChain, LiveKit, or similar frameworks.
- Familiarity with vector databases (Pinecone, Weaviate, pgvector).
- Strong grasp of retrieval-augmented generation (RAG) and evaluation pipelines.
Soft Skills
- Problem solver who thrives in debugging complex systems.
- Able to communicate clearly with both technical and non-technical stakeholders.
- Ownership mindset — accountable for backend stability and ML pipeline performance.
- Comfort working autonomously with little oversight.
Bonus Skills
- Experience with event-driven architectures (Kafka, RabbitMQ).
- Exposure to contract generation, document parsing, or real estate APIs.
- Understanding of privacy-preserving ML (federated learning, differential privacy).
- Experience with cost optimization for LLM deployments.
Experience
- 5+ years in backend or MLOps roles.
- Proven record of deploying production AI/ML applications.
Posted on:
Aug 27, 2025