Senior AI Engineer
Role Overview
Senior AI Engineer role focused on taking business AI problems through solution design, implementation, integration, deployment, and outcome ownership.
The role emphasizes production-ready systems rather than experiments or demos.
Experience: 4+ years. IT education background is mandatory.
Key Skills for This Role
Full Job Posting
Role overview
Senior AI Engineer role focused on taking business AI problems through solution design, implementation, integration, deployment, and outcome ownership.
The role emphasizes production-ready systems rather than experiments or demos.
Experience: 4+ years. IT education background is mandatory.
Location and interview
- The role is on-site in Ahmedabad, Gujarat, India.
- The interview is offline / in person.
What you will do
- Translate business problems into practical AI solutions and build LLM-powered applications and AI agents.
- Build production-grade RAG systems, AI workflows, and intelligent automation.
- Integrate AI with APIs, backend systems, and enterprise applications; work with documents and unstructured data.
- Design prompts, evaluation frameworks, and AI system architectures; deploy and operate solutions on the cloud.
- Partner with engineering and business teams to improve solutions and evaluate models and frameworks for business value.
- Own performance, reliability, scalability, and business outcomes.
Must-have qualifications
- IT education background and 4+ years of relevant professional experience.
- Strong Python development skills and hands-on experience with LLMs and Generative AI.
- Experience building AI agents or agentic AI systems; solid understanding of RAG and prompt engineering.
- REST APIs, backend integration, AI application architecture, and cloud deployment experience.
- Ability to build production-ready systems, not just proofs of concept.
- Willingness to work on-site in Ahmedabad and attend an in-person interview.
Nice to have
- OCR / Document Intelligence; LangGraph, LangChain, LlamaIndex, or CrewAI.
- OpenAI, Claude (Anthropic), or Gemini APIs; Docker and vector databases.
- AI evaluation, observability, and optimization; machine learning background and willingness to train models when needed.
How you work
Independently turn ambiguous problems into working AI solutions and move between business discussions, architecture, coding, and deployment.
Choose pragmatic technologies, take ownership when solutions fail, and communicate clearly with technical and non-technical stakeholders.
Focus on solving business problems and building real-world AI systems that ship.
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