Senior AI Engineer
Spinny · Mumbai
- Experience4–8 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelmid
- Posted2 Sept 2026
About Spinny
Spinny is hiring in Mumbai in automotive mobility. This role looks for around 4+ years of experience.
Skills
- Large Language Models
- OCR
- Retrieval-Augmented Generation
- Agentic AI
- GCP
- Amazon Web Services
- Microsoft Azure
- LLMOps
- fine-tuning
- prompt engineering
- FastAPI
- REST
- Spring Boot
- data pipelines
- vector databases
- Elasticsearch
- MLOps
- CI/CD
- Kubernetes
- Docker
- Jenkins
- Ansible
- ELK Stack
The role
A generative AI engineer at an automotive mobility company designs and deploys LLM-powered workflows, Retrieval-Augmented Generation, and Agentic AI systems, building production solutions with cloud infrastructure and MLOps. The role also develops FastAPI integrations and scalable data pipelines for enterprise knowledge retrieval.
Full job description
We are seeking an experienced Senior AI Engineer to join our Engineering Team. The ideal candidate will have deep expertise in Large Language Models (LLMs), OCR, Retrieval-Augmented Generation (RAG), and Agentic AI systems, coupled with strong experience in cloud-native, enterprise-scale environments. This role is pivotal in driving our AI innovation strategy, leading end-to-end solution development, and delivering production-grade AI systems.
Responsibilities:
Lead the full lifecycle of Generative AI solutions from design and development to deployment, encompassing LLM-powered workflows, RAG pipelines, OCR, and Agentic AI systems.
Architect and implement secure, scalable AI infrastructures using GCP, AWS, or Azure.
Apply LLMOps best practices, including fine-tuning, advanced prompt engineering, and model optimisation, to maximise performance and contextual accuracy.
Design and maintain APIs and microservices (FastAPI, REST, Spring Boot) to integrate AI capabilities into enterprise systems.
Build and optimise data pipelines and manage vector databases and Elasticsearch for efficient knowledge retrieval and decision-making.
Implement MLOps, CI/CD, and DevOps pipelines using Kubernetes, Docker, Jenkins, and Ansible for automated deployment and monitoring.
Ensure system reliability and observability through logging, monitoring, and infrastructure-as-code practices (e. g., ELK stack).
Collaborate cross-functionally with product, engineering, and business teams to align AI solutions with organisational goals.
Mentor and guide junior engineers, setting best practices for scalable, maintainable AI development.