Senior AIML Engineer

• Partner with business, product, and engineering teams to define problem statements, evaluate feasibility, and design AI/ML-driven solutions that deliver measurable business value
• Lead and execute end-to-end AI/ML projects from data exploration and model development to validation, deployment, and monitoring in production
• Independently design and implement scalable machine learning solutions and data systems, ensuring end-to-end workflows, large-scale analytics, and reliability

• Design and implement RAG (Retrieval Augmented Generation) systems for enterprise knowledge management
• Develop guardrails and safety measures for GenAI applications in production
• Implement cost optimization strategies for LLM inference at scale
• Create synthetic data generation pipelines for model training and testing
• Build and optimize prompt engineering strategies and fine-tuning pipelines

• Drive solution architecture using techniques in data engineering, programming, machine learning, NLP, and computer vision
• Implement and refine feature engineering, monitoring, ML pipelines, deploy models in production
• Build real-time inference APIs with sub-second latency requirements
• Develop forecasting models for demand prediction and supply chain optimization
• Create recommendation systems for route optimization and customer solutions

• Champion the scalability, reproducibility, and sustainability of AI solutions by establishing best practices in model development, CI/CD, and performance tracking
• Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability
• Implement comprehensive model versioning, registry, and rollback strategies
• Build automated retraining pipelines and drift detection systems

• Guide junior and associate AI/ML engineers through technical mentoring, code reviews, and solution reviews
• Translate technical outputs into actionable insights for business stakeholders through storytelling and data visualizations
• Drive cross-team and cross-discipline initiatives to optimize workflows and enhance collaboration
• Identify and evangelize the adoption of emerging tools, technologies, and methodologies across teams

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