Senior AI Engineer

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    "datePosted": "2026-03-26",
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    "description": "<p><span style=\"font-weight: bold\">About RedotPay</span></p>\n<p>RedotPay is a global crypto payment fintech integrating blockchain solutions into traditional banking and finance infrastructure. Our user-friendly crypto platform empowers millions globally to spend and send crypto assets, ensuring faster, more accessible, and inclusive financial services. RedotPay advances financial inclusion for the unbanked and supports crypto enthusiasts, driving the global adoption of secure and flexible crypto-powered financial solutions. Join us in shaping the future of finance and making a meaningful impact on a global scale.</p>\n<p><br></p>\n<p><span style=\"font-weight: bold\"><span>The Role</span></span></p>\n<p><span>We are looking for a <span style=\"font-weight: bold\">Senior AI Engineer</span> to join our newly formed AI team. In this role, you will not just be building models; you will be engineering the systems that bring AI to production in a highly regulated environment. You will work closely with payment processors, risk analysts, and software engineers to solve complex problems where a 0.1% error rate has significant financial implications.</span></p>\n<p><br></p>\n<p><span style=\"font-weight: bold\"><span>Key Responsibilities</span></span></p>\n<ul>\n<li><span style=\"font-weight: bold\"><span>Model Development &amp; Deployment:</span></span><span> Design, build, and deploy machine learning models (Classical ML, NLP, and LLMs) to solve payment-specific challenges such as fraud detection, transaction routing optimization, liquidity forecasting, and KYC automation.</span></li>\n<li><span style=\"font-weight: bold\"><span>AI Infrastructure:</span></span><span> Architect and maintain scalable AI pipelines for data preprocessing, feature engineering, model training, and low-latency inference.</span></li>\n<li><span style=\"font-weight: bold\"><span>LLM Integration:</span></span><span> Leverage Large Language Models (OpenAI, Anthropic, or Open Source) for agentic workflows, automated customer support resolution, and document processing (e.g., compliance document extraction), ensuring prompt engineering and RAG (Retrieval-Augmented Generation) architectures are optimized for factual accuracy.</span></li>\n<li><span style=\"font-weight: bold\"><span>Performance &amp; Monitoring:</span></span><span> Implement robust MLOps practices to monitor model drift, data quality, and inference performance. Establish alerting mechanisms for model degradation in production.</span></li>\n<li><span style=\"font-weight: bold\"><span>Cross-functional Collaboration:</span></span><span> Partner with Product, Engineering, and Risk teams to translate business problems (e.g., reducing false positives in fraud alerts) into technical AI roadmaps.</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\"><span>Required Qualifications</span></span></p>\n<ul>\n<li><span style=\"font-weight: bold\"><span>Experience:</span></span><span> Minimum of <span style=\"font-weight: bold\">3 years of professional experience</span> in AI-related development, with a proven track record of deploying models into production environments (not just academic or proof-of-concept work).</span></li>\n<li><span style=\"font-weight: bold\"><span>Engineering Proficiency:</span></span><span> Strong software engineering skills in <span style=\"font-weight: bold\">Python</span>. Experience with version control (Git), CI/CD pipelines, and containerization (Docker/Kubernetes).</span></li>\n<li><span style=\"font-weight: bold\"><span>Machine Learning Depth:</span></span><span> Deep understanding of the ML lifecycle. Experience with time-series forecasting, anomaly detection, or graph neural networks is highly preferred given the payments context.</span></li>\n<li><span style=\"font-weight: bold\"><span>LLM &amp; NLP:</span></span><span> Proven experience building applications using <span style=\"font-weight: bold\">LLMs</span>. This includes proficiency in prompt engineering, RAG pipelines, vector databases (Pinecone, Milvus, Weaviate), and evaluating LLM performance.</span></li>\n<li><span style=\"font-weight: bold\"><span>Database &amp; Big Data:</span></span><span> Strong SQL proficiency and experience working with large-scale datasets using Spark, Snowflake, or BigQuery.</span></li>\n</ul>",
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