Shoppers Stop
Website:
shoppersstop.com
Job details:
Role- AI Engineer
Experience - 1- 4 Years
Education- BE/B.Tech
Responsibilities
- Design, develop & deploy Generative AI models (text, image, and multi-modal) using Python & modern ML frameworks
- Build & optimise LLM-based applications, leveraging techniques such as RAG, prompt engineering, fine-tuning, and embeddings
- Develop AI agents for autonomous task execution using frameworks like LangChain, CrewAI, and N8N
- Design and develop AI-driven web and mobile applications, including backend services and APIs
- Build scalable FastAPI-based microservices for AI workloads
- Integrate GenAI models into business applications, enterprise workflows, and automation pipelines
- Collaborate with frontend teams to integrate Python APIs with Next.js-based applications
- Deploy, monitor, and scale AI solutions on Google Cloud Platform (GCP) and Microsoft Azure
- Work with Vertex AI and cloud-native AI services for training, serving, and orchestration
- Optimise AI workloads for performance, latency, scalability, and cost efficiency
- Implement data pipelines and analytical processes to support model training, evaluation, and monitoring
- Work with structured and unstructured datasets to derive insights and enhance AI outputs
- Document AI architectures, codebases, and operational processes for maintainability and knowledge sharing
- Follow GIT, CI/CD, and ML Ops principles to ensure robust and repeatable deployments
- Collaborate with product, data, and business teams to translate requirements into AI-powered solutions
Preferred competencies & Skills
- Strong Experience with Python and developing Fast API’s.
- Hands-on experience with developing Gen AI Agents using frameworks like Langhain, CrewAI, N8N.
- Frontend Development in Next.JS, Integrating Python API’s in Next JS
- Familiarity with Generative AI Platforms such as Vertex AI.
- Solid understanding of Generative AI concepts like LLM’s, Vector Databases, RAG, RLHF.
- GIT and CI CD principles
- Experience with cloud environments like GCP, Azure.
What Success Looks Like
- Production-grade GenAI solutions deployed at scale
- Reliable, cost-optimised AI workloads in cloud environments
- Business-ready AI applications with measurable user and process impact
- Well-documented, reusable AI components and services
Click on Apply to know more.