Job Description
<p></p><p><b>Description : </b></p><p><br/></p><p><b>Job Title :</b> AI Software Engineer</p><br/><p><b>Location :</b> Bangalore, KA</p><br/><p><b>Job Type :</b> Full-Time (Onsite/Hybrid)</p><br/><p><b>Reports to :</b> IT Head from the Development team</p><br/><p><b>About Fulfillment IQ (FIQ) :</b></p><br/><p>At Fulfillment IQ, were disruptors in the supply chain and logistics sector.</p><br/><p>As an award-winning supply chain tech company, we design and deliver cutting-edge solutions for D2C brands, retailers, and 3PLs.</p><br/><p>Our teams thrive on solving complex logistics challenges, from developing custom software and advising on tech stack selection to implementing advanced supply chain technology.</p><br/><p>If youre passionate about problem-solving, thrive in dynamic environments, and want to make an impact, wed love to have you on board.</p><br/><p><b>Role Overview :</b></p><br/><p>As an AI Software Engineer at Fulfillment IQ, you will design, develop, and deploy intelligent applications that leverage generative AI, machine learning, and natural language processing.</p><br/><p>Youll collaborate with engineers, product managers, and domain experts to deliver scalable solutions across supply chain, logistics, and enterprise automation.</p><br/><p>This role requires expertise in AI/ML model development, software engineering best practices, and deploying AI solutions into production environments.</p><br/><p><b>Key Responsibilities :</b></p><br/><p>- Design and implement AI-powered applications with a focus on NLP, LLM fine-tuning, and generative AI.</p><br/><p>- Develop production-ready code and integrate AI solutions with APIs, databases, and enterprise systems.</p><br/><p>- Build rapid prototypes using AI-assisted coding tools (e.g., Claude, GitHub Copilot, Cursor) to accelerate delivery.</p><br/><p>- Apply software engineering best practices including automated testing, CI/CD, and scalable architecture design.</p><br/><p>- Evaluate, optimize, and monitor AI model performance for accuracy, efficiency, and reliability.</p><br/><p>- Document workflows, prompt engineering strategies, and best practices for future & Skills :</b></p><br/><p>- Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or related field.</p><br/><p>- 3-5 years of professional experience in AI/ML engineering or applied NLP.</p><br/><p>- Strong programming skills in Python (preferred), with experience in frameworks such as TensorFlow, PyTorch, or Hugging Face.</p><br/><p>- Hands-on experience with AI-assisted coding tools (Claude, GitHub Copilot, Cursor, etc.)</p><br/><p>- Solid understanding of software architecture, RESTful APIs, and cloud platforms (AWS, GCP, or Azure).</p><br/><p>- Proficiency with Git and CI/CD pipelines.</p><br/><p>- Strong analytical mindset with the ability to validate and refine AI outputs.</p><br/><p><b>Preferred Qualifications :</b></p><br/><p>- Experience in supply chain, logistics, or enterprise applications.</p><br/><p>- Familiarity with MLOps tools and practices for managing model lifecycle.</p><br/><p>- Exposure to computer vision, recommendation systems, or time-series modeling.</p><br/><p><b>Month-to-Month Activities (Summary) :</b></p><br/><p>- Months 12 : Align with product + engineering on yearly goals.</p><br/><p>- Months 34 : Improve data pipelines and monitoring tools.</p><br/><p>- Months 56 : Train new candidate models.</p><br/><p>- Months 79 : Package and deploy first production-ready models.</p><br/><p>- Months 1012 : Optimize model performance (reduce inference cost/latency).</p><br/><p><b>Key Performance Indicators (KPIs) :</b></p><br/><p>- development efficiency metrics :like cycle time, deployment frequency, change failure rate.</p><br/><p>- AI model performance (accuracy, precision, recall),.</p><br/><p>- business impact metrics (ROI, cost reduction, process automation levels), and quality and reliability metrics (bug rates,.</p><br/><p><b>AI Model Performance KPIs :</b></p><br/><p>- Accuracy, Precision, Recall :Measures of how well the AI model performs its intended function.</p><br/><p>- Model Confidence/Probability Scores :The AI's certainty in its outputs.</p><p></p> (ref:hirist.tech)