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Senior LLM Engineer

Typical $25–60/hr Worldwide Remote · worldwide coding Contract / freelance
Pay rate · Typical $25–60/hr
Typical hourly range for this type of role — the exact rate is confirmed by the hiring company.

Overview

Design and develop scalable Generative AI solutions with Python and Langchain.

Senior LLM Engineer – GenAI / ML (Python, Langchain)

Full Time

Location

India

Overall Experience

7–12 Years

Focus

Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration.

Key Responsibilities

  • Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines.
  • Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions.
  • Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives.
  • Translate business needs into scalable, production-ready AI solutions.
  • Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases.
  • Contribute to architecture discussions, code reviews, and performance optimization.

Skills Required

  • Proficient in Python, Langchain, and SQL.
  • Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows.
  • Background in machine learning or software engineering with a focus on system-level thinking.
  • Experience working with cloud platforms like AWS, Azure, or GCP.
  • Ability to work independently while collaborating effectively across teams.
  • Excellent communication and stakeholder management skills.

Preferred Qualifications

  • 1+ years of hands-on experience in LLMs and Generative AI techniques.
  • Experience contributing to ML/AI product pipelines or end-to-end deployments.
  • Familiarity with MLOps and scalable deployment patterns for AI models.
  • Prior exposure to client-facing projects or cross-functional AI teams.
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