Machine Learning Engineer

SourcingXPress

Hyderabad

On-site

INR 1,000,000 - 2,000,000

Full time

14 days+
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Job summary

SourcingXPress in Hyderabad is looking for a skilled Machine Learning Engineer specializing in Large Language Models and Generative AI technologies. The ideal candidate will design and deploy LLM-based applications, optimize AI solutions, and collaborate with cross-functional teams.

This role demands strong analytical skills, programming expertise in Python or Java, and proficiency with frameworks like LangChain and Hugging Face. Candidates with a passion for AI and problem-solving are encouraged to apply.

Qualifications

  • Strong experience with Large Language Models and Generative AI.
  • Hands-on expertise in LoRA, QLoRA, and LLM fine-tuning techniques.
  • Proficiency in LangChain, LangGraph, and Hugging Face ecosystem.

Responsibilities

  • Design, develop, fine-tune, and deploy LLM-powered applications.
  • Build and optimize Retrieval-Augmented Generation (RAG) pipelines.
  • Collaborate with cross-functional teams to design scalable AI solutions.

Skills

Large Language Models (LLMs)
Generative AI
LoRA
QLoRA
Python
Java
LangChain
LangGraph
Hugging Face
Retrieval-Augmented Generation (RAG)

Job description

Job Description

We are seeking a highly skilled Machine Learning Engineer with hands‑on experience in Large Language Models (LLMs) and Generative AI technologies. The ideal candidate will have expertise in building, fine‑tuning, and deploying LLM-based solutions, along with strong programming skills and a deep understanding of modern AI frameworks and architectures.

This role requires a strong analytical mindset, excellent problem‑solving capabilities, and the ability to evaluate and recommend the most effective approaches, models, and frameworks for diverse business use cases.

Key Responsibilities
  • Design, develop, fine‑tune, and deploy LLM‑powered applications and solutions.
  • Build and optimize Retrieval‑Augmented Generation (RAG) pipelines for enterprise use cases.
  • Implement parameter‑efficient fine‑tuning techniques such as LoRA and QLoRA.
  • Work with frameworks such as LangChain, LangGraph, and Hugging Face to develop scalable AI applications.
  • Train, fine‑tune, evaluate, and optimize foundation models and domain‑specific LLMs.
  • Develop and maintain model deployment pipelines and production‑grade AI systems.
  • Analyze business requirements and recommend appropriate AI/ML architectures and frameworks.
  • Collaborate with cross‑functional teams to design scalable and efficient AI solutions.
  • Contribute to system design discussions and provide high‑level architectural recommendations.
Required Skills
  • Strong experience with Large Language Models (LLMs) and Generative AI.
  • Hands‑on expertise in LoRA, QLoRA, and LLM fine‑tuning techniques.
  • Experience building Retrieval‑Augmented Generation (RAG) applications.
  • Proficiency in LangChain, LangGraph, and Hugging Face ecosystem.
  • Experience with LLM training, evaluation, and optimization.
  • Strong programming skills in Python and/or Java.
  • Experience with model deployment, inference optimization, and productionization.
  • Understanding of vector databases, embeddings, and semantic search concepts.
  • Strong problem‑solving, analytical, and logical reasoning skills.
  • Good understanding of system design, scalability, and distributed architectures.
  • Ability to evaluate multiple technical approaches and select optimal solutions based on business requirements.
Preferred Qualifications
  • Experience with cloud platforms such as AWS, Azure, or GCP.
  • Knowledge of MLOps, CI/CD pipelines, and containerization technologies.
  • Familiarity with AI observability, monitoring, and model performance optimization.
  • Experience building enterprise‑grade GenAI applications.

Company: Quadrant Technologies

Website: Visit Website

LinkedIn: Visit LinkedIn

Business Type: Small/Medium Business

Company Type: Product & Service

Business Model: B2B

Funding Stage: Seed

Industry: Information Technology

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