AI ML Engineer

Overview

This role is centered on developing sophisticated machine learning models and AI-powered applications aimed at addressing intricate business issues. The engineer plays a pivotal part in creating systems that are robust, scalable, and optimized for deployment in real-world scenarios, collaborating closely with various teams to ensure seamless integration of AI solutions into production environments.

Main Responsibilities

• Convert complex business challenges into machine learning problems and choose appropriate model structures such as gradient boosting or transformer architectures, establishing clear criteria for success.
• Develop comprehensive pipelines covering feature extraction, model training, hyperparameter optimization, and packaging to generate reproducible model artifacts.
• Enhance model inference using techniques like quantization, distillation, and mixed precision to improve latency and throughput on CPUs and GPUs.
• Perform extensive model evaluation considering factors beyond accuracy, including calibration, fairness, cost-sensitive metrics, and dealing with data imbalance (PR/ROC analysis).
• Manage MLOps aspects including model versioning, lineage tracking, experiment documentation, and implement deployment strategies like rollbacks and canary releases.
• Create and maintain real-time and batch inference services, integrating them with message queuing systems and vector databases.
• Set up monitoring for schema validation, data drift detection, model performance regression, and cost tracking; establish alerting systems and autoscaling tied to service level agreements, and prepare incident response runbooks.
• Design data contracts and build ETL/ELT pipelines using tools like Spark or Databricks with testing and data backfilling routines.
• Implement data quality gates and schema evolution protocols to ensure data integrity and prevent mismatches.
• Incorporate privacy-by-design principles including personal identifiable information (PII) management, tokenization, and secure handling of secrets.
• Collaborate on designing cost-effective data storage architectures with tiering, caching, and efficient file formats like Parquet and Delta.
• Plan and oversee experimentation frameworks such as A/B testing and counterfactual evaluations, setting guardrails and success metrics alongside product teams.
• Integrate machine learning models through APIs and SDKs, embedding business rules and fallback mechanisms to ensure graceful degradation.
• Document models comprehensively through model cards and decision logs; communicate trade-offs and technical details effectively to stakeholders.

Qualifications and Skills

• A bachelor's or master's degree in Computer Science, Data Science, Artificial Intelligence/Machine Learning, or a related discipline.
• Demonstrated expertise in designing, training, and deploying machine learning models and AI systems.
• Proficient in Python programming and experienced with ML frameworks including TensorFlow, PyTorch, and Scikit-learn.
• Hands-on knowledge of MLOps tools and practices such as Docker, Kubernetes, MLflow, and continuous integration/continuous deployment pipelines.
• Experience working with data processing and ETL technologies like Apache Spark and Databricks, handling large-scale datasets.
• Competence in model optimization techniques including quantization and distillation, with skills in tuning model performance for production environments.
• Familiarity with cloud services such as Microsoft Azure, Amazon AWS, or Google Cloud Platform, including scalable system architecture design.
• Understanding of data governance, privacy regulations, and standards compliance.
• Strong analytical capabilities and problem-solving aptitude with meticulous attention to detail.
• Excellent communication skills for effective collaboration across cross-functional teams and clear technical presentations.