Remote Software Engineer Salary in Indonesia

Toptal Vetted Full-stack software engineer with 15+ of experience delivering mission-critical backend systems for global startups and Fortune 500s.. Worked with many companies in Europe, US and Asia. I am a "product-minded" engineer. I look beyond the ticket to ensure the solution drives user value and system stability as well as improved developer experience for team healthy being.

AWSFull StackPythonReactReact NativeGolangNode.jsMobile App DevelopmentRustTest driven developmentTypeScriptTerraformRESTful APISoftware DevelopmentSoftware architectureAWS DynamoDBAWS LambdaAWS Amplify

Machine learning engineer who works on end-to-end pipeline from data collection, data cleaning, preprocessing, train the model, evaluate the model, and to deployment to production using pytorch. Beside machine learning/ artificial intelligence, I can do front-end development using React/Next, Vue/Nuxt, both using Typescript. And a bit of back-end using NodeJS (AdonisJS)

Machine learningPythonNLPData ScienceComputer VisionVue.jsNuxt.jsReactNext.jsAzureOCRTensorFlowPyTorchC++

Applied AI engineer with 7+ years of hands-on delivery, most of it embedded with client and stakeholder teams — from McKinsey's QuantumBlack and Toptal engagements to biotech, hedge-fund, and trading clients. Works the whole path: scoping the highest-value use case, choosing the architecture (models, agents, retrieval, guardrails), shipping a prototype fast, proving it with evals, and hardening it for production. Production AI systems. Built an end-to-end pathology ML system on AWS for a global biobank; a 50+ agent autonomous trading platform running live capital behind a fail-closed governance layer; conversational agents that plan and act through browser and desktop automation (Pydantic AI, Playwright); and RAG and semantic search backends (LangChain, LlamaIndex, FastAPI) on Azure. Fine-tunes open-source foundation models with LoRA/PEFT and SFT — H-Optimus-0 (vision), Kimi-Audio (audio), FinGPT and FinRobot (finance) — and serves them with vLLM and NVIDIA Triton. Evaluation. Treats evals as the backbone of any AI launch: representative datasets, programmatic graders and rubric checks, reference solutions and negative controls, and repeated-rollout analysis (pass@k, trajectories, failure modes). Most recently designed RL environments and verifiable tasks for evaluating frontier coding agents, contributing to Toptal's ExpertCrowd SWE Benchmark. Reliability, cost, and governance. Cut LLM pipeline token costs 50% with the OpenAI Batch API, rewrote latency-critical paths in Rust, and runs MLOps automation (Kubeflow, Vertex AI, MLflow, CI/CD) for continuous training and monitoring. Designs for enterprise requirements from day one — access control, zero-trust networking, encryption, audit trails, and data-usage policies. Has led data and MLOps teams, mentored engineers, and translated technical results into business decisions for executives — on GCP, AWS, and Azure, in Python, TypeScript, Go, and Rust.

Google Cloud PlatformAWSPythonMLOpsTypeScriptRustPyTorchMachine LearningLLMDockerReinforcement LearningPlaywright