Senior ML Engineer

<div><span><div><span style="font-size: inherit;"><strong>Perfect Fit</strong>:</span></div><div><span style="font-size: inherit;">&nbsp;</span></div><ul><li>A <strong>Bachelor’s or Master’s degree</strong> in Computer Science, Machine Learning, Data Science, Software Engineering, or a related quantitative field.</li><li><strong>4+ years of experience</strong> in designing, building, and deploying end-to-end machine learning pipelines in production environments.</li><li>Proficiency in <strong>programming languages</strong> such as <strong>Python</strong>, <strong>PySpark</strong>, and/or <strong>Scala</strong> for scalable systems.</li><li>Strong expertise in <strong>machine learning frameworks</strong> such as <strong>scikit-learn</strong>, <strong>XGBoost</strong>, <strong>and</strong> <strong>PyTorch</strong>, with hands-on experience in training, tuning, and deploying machine learning models.</li><li>Practical knowledge of <strong>data preprocessing and feature engineering</strong>, with experience in tools like <strong>Pandas</strong>, <strong>NumPy</strong>, and <strong>Dask</strong> for handling large datasets.</li><li>Proven experience deploying models in <strong>production environments</strong>, using tools like <strong>Docker</strong>, <strong>Kubernetes</strong>, and cloud services (<strong>AWS, Azure</strong>).</li><li>Expertise in <strong>MLOps practices</strong>, including <strong>CI/CD pipelines</strong>, <strong>model versioning</strong>, and <strong>monitoring</strong>, using tools like <strong>MLFlow</strong>, <strong>Kubeflow</strong>, or <strong>TensorFlow Extended (TFX)</strong>.</li><li>Familiarity with <strong>database technologies</strong>, including <strong>SQL</strong>, <strong>NoSQL</strong> (e.g. MongoDB, Cassandra), and <strong>time-series databases</strong> (e.g. InfluxDB).</li><li>Knowledge of <strong>APIs and integration</strong>, including building and consuming <strong>RESTful APIs</strong> for model serving.</li><li>Strong understanding of <strong>cloud platforms</strong> (AWS, GCP, Azure) and <strong>orchestration tools</strong> (e.g. Airflow) for workflow automation.</li><li>Solid foundation in <strong>data structures and software engineering best practices</strong>, including version control with <strong>Git</strong>.</li></ul><p style="margin-left: 40px;"><span style="font-size: inherit;">&nbsp;</span></p><div><span style="font-size: inherit;"><strong>Nice-to-Have</strong>:</span></div><div><span style="font-size: inherit;">&nbsp;</span></div><ul><li>Experience with <strong>feature stores</strong> (e.g., Feast, Hopsworks) to manage and reuse machine learning features.</li><li>Hands-on experience with <strong>LLMOps tools</strong> and deploying large-scale models like LLMs (e.g. GPT, LLaMA) in production.</li><li>Familiarity with <strong>graph databases</strong> (e.g., Neo4j) or <strong>vector databases</strong> (e.g., Pinecone, FAISS) for advanced search and retrieval tasks.</li></ul></span></div>