Machine Learning Operations (MLOps) Engineer / Software Engineering Manager

Machine Learning Operations (MLOps) Engineer / Software Engineering ManagerBoost Capital • Work From Anywhere • Full-time

About Boost CapitalAt Boost, we build the AI infrastructure powering digital onboarding for banks and microfinance institutions across Southeast Asia. We enable financial institutions to onboard clients digitally for loans and savings in 5–10 minutes through chat, without app downloads.Traditionally, banking meant physical branches and long forms. Boost turns that on its head, with chat-based lending, document AI, and agentic workflows that make financial services 100x faster and radically more accessible. We integrate with a new partner bank in 2–3 weeks, helping them expand their reach instantly. Backed by top-tier institutional and angel investors, Boost is building the digital rails for inclusive finance in emerging markets.

Position Details

Field: AI

Position: Machine Learning Operations (MLOps) Engineer / SWE Engineering Manager

Location: Work From Anywhere (teams across Asia, Europe, North America)Assignment Category: Full-time

Reporting Line: Chief AI Officer

What You'll Do

• Own the production AI platform end to end: model deployment, zero-downtime rollouts, autoscaling, and cost efficiency across containerized cloud infrastructure.

• Lead and grow a distributed team of ML, backend, and DevOps engineers: set technical direction, mentor engineers, review code, and hold the bar on quality and delivery.

• Architect and maintain the pipelines that carry data and models from training to serving: orchestration, feature management, data validation, drift monitoring, and reproducible retraining.

• Set the engineering standard for CI/CD, testing, observability, and incident response in ML systems, and own the on-call posture for model latency, drift, and reliability.

• Own security and compliance for AI systems (least-privilege access, encryption, network isolation, and model versioning and lineage) while partnering with data science, product, and leadership to move models from prototype to production and translate performance into business impact.

Required Qualifications

• 8–10 years in industry software engineering, including 4+ years running ML systems in production.

• 2+ years leading engineers as a tech lead or manager: setting direction, mentoring, and owning delivery.

• Expert-level Python, with production-grade testing, packaging, and code review practices.

• Deep Docker and Kubernetes expertise: scaling, manifests, ingress, networking, and debugging.

• Proven record serving ML workloads on a major cloud platform, with ownership of cost and reliability.

• Command of the production ML lifecycle: orchestration, versioning, evaluation, retraining, and drift.

• Experience architecting data pipelines: batch and streaming ingestion, schema evolution, and lineage.

• Strong CI/CD and infrastructure-as-code practice, including progressive delivery and rollback strategy.

• Observability and incident response ownership: metrics, logging, tracing, alerting, and on-call culture.

• Security fundamentals: least-privilege access control, encryption at rest and in transit, and isolation.

• Excellent communication skills: aligning technical decisions with business outcomes and driving consensus.

Preferred Qualifications

• Google Cloud Platform, particularly GKE, Cloud Run, Vertex AI (Pipelines, Feature Store), Pub/Sub, and Dataflow.

• CI/CD with GitHub Actions, Cloud Build, and Artifact Registry, including blue/green and canary releases with rollback gates.

• Hands-on with TFX or Kubeflow.

• Experience with TensorFlow, PyTorch, or JAX.

• Observability tooling, such as Prometheus, Grafana, Sentry, DataDog, or Cloud Monitoring.

• Familiarity with Go and Bash

• Familiarity with VPC-SC, IAM, and CMEK on GCP.

• Experience in regulated environments (GDPR, SOC 2, BSP).

• Background in fintech, document AI, computer vision, NLP, or fraud detection.