AI Engineer - FullStack | Scrabble
Posted on February 4, 2026
Job Description
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<h2 node="[object Object]">Company Overview</h2>
<p>We are an innovative lab focused on artificial intelligence, working with large financial institutions to expand and enhance our AI product offerings. Our culture emphasizes collaboration, creativity, and cutting-edge technology.</p>
<h2 node="[object Object]">Job Summary</h2>
<p>The AI Engineer – Full Stack role involves contributing to our AI innovation lab team by designing, developing, and deploying advanced AI-driven applications tailored for the banking and financial services industry. This position offers the opportunity to collaborate with top domain experts to create impactful solutions.</p>
<h2 node="[object Object]">Responsibilities</h2>
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<li node="[object Object]">Design, develop, and deploy <strong>end-to-end AI-driven applications</strong> using modern AI technology stacks.</li>
<li node="[object Object]">Fine-tune and implement <strong>Large Language Models (LLMs)</strong> for practical applications within the Banking, Financial Services, and Insurance (BFSI) sector.</li>
<li node="[object Object]">Build intelligent <strong>multi-agent systems</strong> and <strong>multi-step reasoning pipelines</strong> to solve complex problems.</li>
<li node="[object Object]">Develop sophisticated workflows utilizing frameworks like <strong>LangChain</strong>, <strong>LangGraph</strong>, and similar tools to enhance application performance.</li>
<li node="[object Object]">Collaborate with cross-functional teams to integrate backend systems using technologies such as <strong>FastAPI</strong>, <strong>Node.js</strong>, and others.</li>
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<h2 node="[object Object]">Qualifications</h2>
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<li node="[object Object]">Hands-on experience with <strong>AI Agents</strong> and <strong>LLM-based applications</strong>.</li>
<li node="[object Object]">Proficiency in <strong>Python</strong> and experience with backend integration, specifically using <strong>FastAPI</strong> and <strong>Node.js</strong>.</li>
<li node="[object Object]">Understanding of <strong>LLM fine-tuning</strong>, deployment, and inference optimization processes.</li>
<li node="[object Object]">Experience with <strong>LangChain</strong>, <strong>LangGraph</strong>, and <strong>RAG pipelines</strong>.</li>
<li node="[object Object]">Degree in Computer Science, Engineering, or a related field.</li>
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<h2 node="[object Object]">Preferred Skills</h2>
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<li node="[object Object]">Familiarity with cloud platforms and services (e.g., AWS, Google Cloud, Azure).</li>
<li node="[object Object]">Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch).</li>
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<h2 node="[object Object]">Experience</h2>
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<li node="[object Object]">A minimum of <strong>3-5 years</strong> of relevant experience in software development, particularly in AI or machine learning applications.</li>
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<h2 node="[object Object]">Environment</h2>
<p>This role typically involves a <strong>hybrid work setting</strong>, combining remote collaboration with occasional in-office meetings. Candidates should be comfortable with a fast-paced and dynamic development environment.</p>
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<h2>What we're looking for</h2>
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<li>Hands-on experience designing, fine-tuning, and deploying AI agents and LLM-based applications in BFSI or related domains.</li>
<li>Ability to design, develop, and deploy end-to-end AI-driven applications using modern AI technology stacks.</li>
<li>Experience building intelligent multi-agent systems and multi-step reasoning pipelines to solve complex problems.</li>
<li>Proficiency with AI workflow frameworks such as LangChain, LangGraph, and RAG pipelines to enhance application performance.</li>
<li>Proficiency in backend integration using Python, FastAPI, and Node.js for AI application deployment.</li>
<li>Relevant professional experience in software development, AI, or machine learning applications (3-5 years preferred).</li>
<li>Educational background in Computer Science, Engineering, or related fields from recognized institutions.</li>
<li>Familiarity with cloud platforms (AWS, GCP, Azure) and machine learning frameworks like TensorFlow or PyTorch.</li>
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