AI/ML Engineer

Role Overview: As an AI/ML Full Stack Engineer, you will be responsible for implementing end-to-end, AI-powered solutions. Your primary focus will be on building and integrating AI solutions, full stack development, conversational flow & orchestration, data engineering & infrastructure, and cross-functional collaboration. Key Responsibilities: - Developing, training, and maintaining machine learning models such as NLP and recommendation engines for personalized product recommendations and conversational interfaces. - Implementing scalable pipelines to process and analyze large datasets including product data, user interactions, and logs. - Designing REST or GraphQL APIs to enable real-time conversational interactions. - Creating frontend components using React, Vue.js, or similar technologies to incorporate chat widgets, voice interfaces, and AI-driven product discovery features. - Optimizing backend services in Node.js, Python, or Go for enhanced performance and reliability. - Collaborating with Product and UX teams to design user-centric conversational flows. - Evaluating and integrating state-of-the-art NLP libraries and frameworks for intent detection, sentiment analysis, and context management. - Working with DevOps to deploy models and services on cloud platforms using container orchestration tools. - Implementing CI/CD pipelines for smooth deployments and continuous model updates. - Ensuring robust data security and privacy in compliance with relevant regulations. - Partnering with product managers, designers, and other engineers to define technical solutions. - Participating in code reviews, architectural planning, and knowledge-sharing sessions. Qualifications Required: - Hands-on experience in AI/ML, particularly in NLP and recommendation systems. - Proficiency in a modern backend language (Python, Node.js, Go, or Java) and familiarity with frontend frameworks like React, Vue.js, or Angular. - Experience with cloud services for deploying and scaling AI applications, as well as knowledge of database technologies and data warehousing solutions. - Understanding of ML Ops practices such as model versioning, A/B testing, monitoring, and retraining pipelines. - Expertise in Conversational AI & NLP, and a Full Stack Mindset will be highly valued. - Knowledge in eCommerce, recommendation systems, or retail AI solutions, exposure to LangChain, LLM fine-tuning, or generative AI approaches, and a background in UI/UX or conversational design best practices would be considered a bonus.,