Experience: 4–7 years of experience in software engineering, with at least 2 years focused on AI/ML engineering.
Minimum Education: Bachelor’s degree in Computer Science, Information Technology, Mathematics, or a related field. A Master’s degree is considered an advantage.
Responsibilities
AI System Architecture & Development
- Design and build end-to-end AI systems — from data ingestion, model serving, API layers, to monitoring — following production-grade standards.
- Build and manage LLM integration layers, including RAG (Retrieval-Augmented Generation) architecture, vector databases, and prompt management systems.
- Develop backend services and middleware that expose AI capabilities through secure, well-documented, and high-performing APIs.
- Design and implement data alignment pipelines to ensure incoming data is clean, relevant, and meets required quality standards for AI models.
- Integrate AI models (including computer vision, NLP, and ML models) into existing enterprise systems without disrupting business operations.
Enterprise System Integration & Modernization
- Lead system modernization initiatives by building bridges between legacy systems and AI-powered services through API abstraction layers.
- Integrate AI capabilities with enterprise platforms such as ERP, CRM, data warehouses, and messaging systems.
- Build and manage event-driven architectures to process real-time data required by AI models.
- Design AI scalability strategies to support enterprise workloads by optimizing latency, throughput, and operational costs.
Cloud, Infrastructure & DevOps
- Design and manage cloud infrastructure (AWS, GCP, Azure) for AI workloads, including GPU instances, managed ML services, and serverless inference.
- Build CI/CD pipelines for AI systems, including automated testing, model validation, and staged deployment.
- Implement infrastructure-as-code practices (Terraform, Pulumi) to create reproducible AI environments.
- Manage AI workload containerization using Docker and Kubernetes to ensure scalability and portability.
Security, Compliance & Governance
- Implement security best practices for AI systems, including API authentication, data encryption, model access control, and audit logging.
- Ensure AI solutions comply with relevant data regulations (Indonesia’s PDP Law, GDPR where international exposure exists) and internal company policies.
- Design and implement AI observability capabilities, including bias monitoring, drift detection, explainability, and alerting.
- Collaborate with security and compliance teams to conduct security reviews and penetration testing for AI systems.
Technical Leadership
- Lead technical design reviews and architecture decisions for new AI initiatives.
- Mentor junior and mid-level engineers on AI engineering best practices.
- Collaborate closely with AI Product Managers to translate product requirements into technical specifications.
Qualifications & Requirements
Must Have
- Advanced proficiency in Python for backend development and ML engineering, with a solid understanding of software design patterns.
- Experience building and deploying production-grade AI systems using LLM APIs, RAG architecture, and vector databases.
- Expertise in API development (REST, GraphQL) and microservices architecture.
- Experience with at least one major cloud platform (AWS, GCP, or Azure), including managed AI/ML services (SageMaker, Vertex AI, or Azure ML).
- Strong understanding of data engineering concepts, including ETL pipelines, data lakes, message queues (Kafka/RabbitMQ), and streaming data.
- Experience with containerization (Docker, Kubernetes) for production AI workloads.
- Understanding of AI security practices, including API security, data privacy, and model governance.
- Ability to design systems that are scalable, fault-tolerant, and maintainable.
Nice to Have / Preferred
- Experience integrating AI into enterprise systems (ERP, CRM, legacy systems) within regulated environments.
- Understanding of MLOps practices including model versioning, A/B testing, and canary deployment for AI models.
- Experience with production-grade computer vision or NLP frameworks (TensorFlow Serving, ONNX Runtime, Triton).
- Familiarity with AI agent frameworks (LangGraph, AutoGen) for building complex multi-step AI workflows.
- Experience using observability tools (Datadog, Prometheus, Grafana) for AI system monitoring.
- Cloud certifications such as AWS Solutions Architect, GCP Professional ML Engineer, or Azure AI Engineer are considered a plus.
- Contributions to open-source AI projects or a demonstrable portfolio of AI projects.
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.
