Machine Learning Engineer – AWS & MLOps

<div align="center" style="text-align:center;"><hr style="width:579.0pt;" align="center"></div><div><strong>Job Summary:</strong></div><div>We are seeking a highly skilled and motivated <strong>Machine Learning Engineer</strong> with a strong foundation in programming and machine learning, hands-on experience with <strong>AWS Machine Learning services (especially SageMaker)</strong>, and a solid understanding of <strong>Data Engineering and MLOps practices</strong>. You will be responsible for designing, developing, deploying, and maintaining scalable ML solutions in a cloud-native environment.</div><div align="center" style="text-align:center;"><hr style="width:579.0pt;" align="center"></div><div><strong>Key Responsibilities:</strong></div><ul style="margin-top:0cm;" type="disc"><li>Design and implement machine learning models and pipelines using <strong>AWS SageMaker</strong> and related services.</li><li>Develop and maintain robust <strong>data pipelines</strong> for training and inference workflows.</li><li>Collaborate with data scientists, engineers, and product teams to translate business requirements into ML solutions.</li><li>Implement <strong>MLOps best practices</strong> including CI/CD for ML, model versioning, monitoring, and retraining strategies.</li><li>Optimize model performance and ensure scalability and reliability in production environments.</li><li>Monitor deployed models for drift, performance degradation, and anomalies.</li><li>Document processes, architectures, and workflows for reproducibility and compliance.</li></ul><div align="center" style="text-align:center;"><hr style="width:579.0pt;" align="center"></div><div><strong>Required Skills &amp; Qualifications:</strong></div><ul style="margin-top:0cm;" type="disc"><li>Strong programming skills in <strong>Python</strong> and familiarity with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).</li><li>Solid understanding of <strong>machine learning algorithms</strong>, model evaluation, and tuning.</li><li>Hands-on experience with <strong>AWS ML services</strong>, especially <strong>SageMaker</strong>, S3, Lambda, Step Functions, and CloudWatch.</li><li>Experience with <strong>data engineering tools</strong> (e.g., Apache Airflow, Spark, Glue) and <strong>workflow orchestration</strong>.</li><li>Proficiency in <strong>MLOps tools</strong> and practices (e.g., MLflow, Kubeflow, CI/CD pipelines, Docker, Kubernetes).</li><li>Familiarity with monitoring tools and logging frameworks for ML systems.</li><li>Excellent problem-solving and communication skills.</li></ul><div align="center" style="text-align:center;"><hr style="width:579.0pt;" align="center"></div><div><strong>Preferred Qualifications:</strong></div><ul style="margin-top:0cm;" type="disc"><li>AWS Certification (e.g., AWS Certified Machine Learning – Specialty).</li><li>Experience with real-time inference and streaming data.</li><li>Knowledge of data governance, security, and compliance in ML systems.</li></ul><div><br></div>