Experience: 4–7 years of experience in DevOps or software engineering, with at least 2 years specifically focused on ML operations.
Minimum Education: Bachelor’s degree in Computer Science, Information Technology, or a related field
Responsibilities
ML Pipeline & Infrastructure
- Design, build, and manage end-to-end ML pipelines — from data ingestion, feature engineering, and model training to deployment.
- Build and maintain feature stores to ensure consistency between training and inference data.
- Implement data versioning (DVC) and experiment tracking (MLflow, Weights & Biases) to support reproducibility.
- Manage compute infrastructure for model training, including GPU clusters, distributed training environments, and cost optimization.
- Build LLMOps pipelines for generative AI use cases, including prompt versioning, RAG pipeline management, and vector database operations.
Model Deployment & CI/CD
- Build and manage CI/CD pipelines specifically for ML models, including automated testing, model validation, staging, and production deployment.
- Implement various deployment strategies such as blue-green deployment, canary releases, and shadow deployment to minimize operational risk.
- Package models into containerized inference services using Docker and deploy them to Kubernetes or managed ML platforms.
- Optimize models for inference through quantization, distillation, batching, and caching to reduce latency and cost.
- Manage model registries and model versioning to ensure traceability across deployments.
Model Monitoring & Reliability
- Build comprehensive monitoring systems to detect model drift, data drift, and performance degradation in real time.
- Design and implement automated retraining triggers based on predefined performance thresholds.
- Establish alerting and on-call procedures for production model incident response.
- Conduct root cause analysis for model failures and coordinate remediation efforts with ML Engineering teams.
Governance, Security & Compliance
- Implement model governance practices including audit trails, approval workflows, and rollback mechanisms.
- Ensure data used within ML pipelines complies with applicable data privacy regulations (Indonesia PDP Law, GDPR where applicable).
- Build observability capabilities to support AI fairness monitoring and bias detection in production environments.
- Collaborate with security teams to conduct vulnerability assessments on ML infrastructure.
Platform Engineering & Collaboration
- Build and maintain internal ML platforms used by Data Science and ML Engineering teams.
- Document platform capabilities, operational runbooks, and internal usage guidelines.
- Collaborate with ML Engineers to ensure developed models can be deployed and maintained effectively.
Qualifications & Requirements
Must Have
- Advanced proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or scikit-learn), although not necessarily at the level of an ML researcher.
- Strong experience with DevOps practices, including CI/CD (Jenkins, GitLab CI, GitHub Actions) and infrastructure as code (Terraform).
- Expertise in containerization and orchestration technologies, including production-grade Docker and Kubernetes environments.
- Experience using MLOps tools such as MLflow, Kubeflow, Metaflow, Ray, or equivalent platforms.
- Experience with at least one cloud platform (AWS SageMaker, GCP Vertex AI, or Azure ML) for managed ML operations.
- Strong understanding of data engineering concepts including data pipelines, feature stores, and data quality monitoring.
- Experience building monitoring and alerting systems for production ML models using tools such as Prometheus, Grafana, or Datadog.
- Strong troubleshooting skills to diagnose and resolve issues in production ML environments.
Nice to Have / Preferred
- Experience with LLMOps, including managing LLM deployment, prompt versioning, and production-grade RAG pipelines.
- Familiarity with distributed computing frameworks (Spark, Ray) for large-scale data processing.
- Experience with model optimization techniques such as ONNX, TensorRT, and quantization for inference efficiency.
- Knowledge of AI/ML governance frameworks and evolving regulatory requirements (including the EU AI Act as a global reference point).
- Experience in industries with strict compliance requirements (banking, insurance, healthcare) is considered a plus.
- MLOps or cloud ML platform certifications (Databricks ML Associate, GCP Professional ML Engineer, or equivalent) are considered an advantage.
BINAR Academy aims to unlock and channel human potential so we can help the world turn into a better place. One of the ways we strive to do that is to channel potential talents to impactful opportunities using our very own job connector, BINAR Job Connect.
BINAR Job Connect has connected up to 250 talents & candidates; whether it be young & aspirational fresh graduate to tech-savvy team leaders, individuals who decided to start from scratch, and junior engineers to skilled product managers to our experienced hiring partners. BINAR Job Connect provides various options of employment, as we intend that everyone processed by us can choose the best career track for them.
