AI Software Architect Position

2 weeks ago

Singapore beBeeArtificial Full time $120,000 - $150,000
Lead AI Software Architect

We are seeking an experienced Lead AI Software Architect to design and develop advanced autonomous AI agents that collaborate with computer vision engineers.

Key Responsibilities:
  • Architect scalable, modular Python libraries and a command-line interface (CLI) that expose core agent primitives – task graphs, skills, memory, and tool interfaces.
  • Implement a highly efficient orchestration layer (e.g., Celery, Argo Workflows, Prefect) that runs multi-step computer vision pipelines with retry, rollback, and service-level agreement (SLA) guarantees.
  • Develop CLI utilities and RESTful APIs that enable engineers to trigger, inspect, and debug agent runs.
  • Maintain continuous integration/continuous deployment (CI/CD) pipelines, comprehensive test suites, and infrastructure-as-code to ensure reliable deployments on a bi-weekly cadence.
Required Skills:
  • Solid engineering foundation – At least 3+ years writing production software (ideally in Python), strong grasp of algorithms, data structures, Git workflows, and code-review best practices.
  • Agent frameworks – hands-on experience designing or extending agent stacks such as LangChain, AutoGen, CrewAI, or custom in-house task-graph engines.
  • Orchestration at scale – proficiency with a workflow scheduler or task queue (e.g., Prefect, Argo Workflows, Airflow, Dagster, Celery) and the patterns for retry, rollback, and SLA tracking.
  • Computer vision pipeline know-how – practical exposure to training and evaluating computer vision models (classification, detection, segmentation) and understanding of data-quality pitfalls.
  • Evaluation & observability – ability to build automated test/evaluation harnesses using pytest, MLflow, wandb, or equivalent, and expose metrics via Prometheus/Grafana or OpenTelemetry.
  • Vector & hybrid search – experience integrating stores such as Pinecone, Weaviate, pgvector, or FAISS to power agent memory and retrieval workflows.
  • Model serving & packaging – familiarity with TorchServe, Triton, BentoML, ONNX Runtime, or similar frameworks, plus Docker/Kubernetes fundamentals.
  • CI/CD & IaC – competence setting up GitHub Actions/GitLab CI pipelines and Infrastructure-as-Code (Terraform, Pulumi) to keep releases predictable.
  • Cloud fluency – production deployments on one or more providers (AWS, GCP, Azure) and an eye for cost/performance trade-offs.
  • Clear communication – comfort writing design documents/RFCs and mentoring peers on agent architecture, testing, and deployment best practices.
Nice-to-Have Skills:
  • Portfolio of AI/computer vision/agent projects or open-source contributions.
  • UI development experience (e.g., Gradio, Streamlit).
  • ML observability tools familiarity (e.g., Grafana or Datadog).

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