AI/Machine Learning Engineer

📌 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. 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.