Principal AI Engineer - Computer Vision (2 years contract)

Principal AI Engineer - Computer Vision (2 years contract) We build autonomous AI agents that partner with computer‑vision engineers to curate data, train models, and ship services—on time, every sprint. A transparent roadmap, bi‑weekly reviews, and robust CI/CD keep us laser‑focused on production impact. This is a 2-year contract position (convertible if good performance) based in Singapore. Key Responsibilities Agent Framework & Libraries Architect modular Python libraries and a CLI that expose core agent primitives—task graphs, skills, memory, and tool interfaces. Orchestration & Scheduling Implement a scalable orchestration layer (Celery, Argo Workflows, Prefect, or similar) that runs multi‑step CV pipelines with retry, rollback, and SLA guarantees. Integrate vector and hybrid search stores so agents can retrieve data during execution. Create CLI utilities and REST/gRPC APIs that let engineers trigger, inspect, and debug agent runs. Maintain CI/CD pipelines, comprehensive test suites, and infrastructure‑as‑code so the agent platform ships reliably on a bi‑weekly cadence. Integrate CV Toolkits Wrap best‑in‑class vision components (OpenCV, TorchVision, MMDetection, Ultralytics YOLO, Albumentations, etc.) so agents can call data‑prep, augmentation, model‑zoo, and metric utilities on demand to meet user requirements. Must-Have Skills Solid engineering foundation – 5 years writing production software (ideally 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 (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 CV 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 docs/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 ML observability tools familiarity (e.g., Grafana or Datadog) Seniority level Mid-Senior level Employment type Contract Job function Information Technology Industries Defense and Space Manufacturing Referrals increase your chances of interviewing at ST Engineering by 2x Sign in to set job alerts for “Artificial Intelligence Engineer” roles. J-18808-Ljbffr

Location: Singapore, SG

Posted Date: 5/23/2025