📌 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.