Senior LLM Engineer
Overview
A remote role based in India centers on designing and building Generative AI and LLM systems with Python and Langchain. The engineer will create RAG pipelines, prompt techniques, and agent-based workflows that run in production. The position expects 7-12 years of experience and close work with engineering teams, business SMEs, and data teams to shape the LLM roadmap. Strong SQL and cloud familiarity across AWS, Azure, or GCP support the day-to-day work.
What You'll Do6
- 1Build and deploy GenAI and LLM systems using Langchain, RAG pipelines, and prompt engineering methods.
- 2Implement agent-based frameworks that deliver context-aware, production-ready solutions.
- 3Define and drive the technical roadmap for LLM initiatives together with the engineering team.
- 4Turn business needs into scalable AI solutions that can handle real-world use cases.
- 5Align AI models with business priorities by working directly with subject-matter experts and data teams.
- 6Participate in architecture reviews, code reviews, and performance tuning across AI services.
Requirements6
- 17-12 years of experience in software engineering or machine learning, with strong Python and SQL proficiency.
- 2Hands-on knowledge of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows.
- 3Working experience with at least one major cloud platform: AWS, Azure, or GCP.
- 4At least 1 year of applied experience with LLMs and Generative AI techniques.
- 5Familiarity with MLOps, scalable deployment patterns, and product pipelines for AI models.
- 6Ability to work independently and communicate clearly with technical and business stakeholders.
Who Should Apply
The ideal candidate has 7-12 years of software or ML experience and can point to shipped GenAI work using Langchain, RAG, or agent frameworks. This role suits engineers who enjoy owning LLM features from design to deployment and can explain trade-offs to business stakeholders. The role is less suitable for junior developers or ML practitioners who have not yet built production AI systems. Applications often score low when they list only coursework or demo projects, or when they cannot name the cloud platform and MLOps practices used in a real deployment. Strong fit signals include concrete metrics, such as inference latency, retrieval accuracy, or system scale.
Location
Required Skills
Application Tip
In your application, describe a production RAG or agent-based system you built with Langchain, including scale, latency, and business impact. Name the cloud platform you used and mention any MLOps tooling for deployment.
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1Application received
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