Job Description

📌 CONTRACT DETAILS

• Duration: 12-months fixed term (subject to change based on business needs)

• Engagement: Through 3rd party/vendor

• Work setup: Hybrid with dedicated full office hours

Lead/Senior

• Lead the end-to-end design, development, and deployment of ML models and AI systems across multiple product lines.
• Strategize the AI/ML roadmap in collaboration with product, engineering, and data leadership — aligning technical investments with business objectives.
• Authorize architectural decisions for ML infrastructure, including model serving, feature stores, and data pipelines.
• Synergize efforts across data science, software engineering, and platform teams to ensure seamless integration of AI capabilities into production.
• Negotiate technical trade-offs and recommend approaches that balance speed, scalability, accuracy, and cost.
• Evaluate and recommend emerging AI/ML technologies and frameworks to continuously raise team capabilities.
• Lead model performance reviews, root cause analyses for model degradation, and drive remediation strategies.
• Formulate and enforce MLOps best practices — CI/CD for ML, model versioning, monitoring, and retraining pipelines.
• Mentor and guide junior and mid-level engineers, fostering a culture of technical excellence.
• Plan and control project timelines, resources, and delivery milestones for AI/ML workstreams.

Mid/Junior

• Plan and implement ML models for tasks such as classification, regression, NLP, or computer vision under senior guidance.
• Control data preprocessing and feature engineering pipelines to ensure high-quality model inputs.
• Evaluate model performance using appropriate metrics and recommend improvements based on experimental results.
• Formulate and run experiments to test hypotheses; document and share findings with the team.
• Contribute to ML workflows covering data ingestion, model training, validation, and basic deployment steps.
• Recommend tools or approaches to solve specific ML challenges, backed by research and prototyping.
• Plan and maintain clear documentation for models, experiments, and data pipelines.
• Collaborate with data engineers, software engineers, and product managers to understand requirements and deliver solutions.
• Continuously evaluate new techniques and research papers relevant to the team's domain.

Requirements

Lead/Senior

• 7+ years of hands-on experience in ML, AI engineering, or a closely related field, with a track record of production-grade ML systems.
• Deep expertise in ML frameworks such as TensorFlow, PyTorch, or JAX, and experience with large-scale model training and optimization.
• Strong proficiency in Python plus MLOps tooling (MLflow, Kubeflow, Airflow, or equivalent).
• Proven experience designing and operating ML serving platforms (TorchServe, Triton, Vertex AI, SageMaker) including monitoring and retraining.
• Expertise with large-scale distributed data processing (Spark, Dask, or similar).
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and container orchestration (Docker, Kubernetes).
• Deep background in NLP, computer vision, recommendation systems, or time-series forecasting.
• Strong statistical modeling, feature engineering, and model evaluation skills.
• Demonstrated ability to communicate complex technical concepts to non-technical stakeholders and influence strategic decisions.
• Experience leading technical teams or serving as tech lead on cross-functional AI projects.
• Good communication skills in Bahasa Indonesia and English, written and spoken.

Mid/Junior

• 1–4 years of experience in ML, data science, or AI engineering, with at least one project delivered in a professional or academic setting.
• Solid understanding of core ML concepts: supervised/unsupervised learning, model evaluation, overfitting, and regularization.
• Proficiency in Python with hands-on experience using scikit-learn, TensorFlow, PyTorch, or equivalent.
• Familiarity with data manipulation using pandas, NumPy, and SQL.
• Basic understanding of MLOps concepts — experiment tracking, model versioning, pipeline automation (MLflow, DVC, or similar).
• Exposure to cloud platforms (AWS, GCP, or Azure) and version control with Git.
• Strong analytical mindset; able to formulate experiments and interpret results critically.
• Eagerness to learn and grow in a fast-paced engineering environment.
• Good communication skills in Bahasa Indonesia and English, written and spoken.
• Ability to work collaboratively in a cross-functional team and take direction constructively.