JOB DETAILS:

Job Role: Machine Learning Engineer - LLM & RAG

Industry: IT MNC

Function: Data Engineering

Salary: Best in Industry

Experience: 5-15 years

Location: Fully Remote Opportunity

MANDATORY CRITERIA:

• Strong Data Engineer Profile
• 5+ YOE in statistical machine learning, deep learning, data mining
• 2+ YOE working with large language models (LLMs)
• Proficiency in Python + at least one other programming language
• Prompt engineering and retrieval-augmented generation (RAG) techniques
• Experience with frameworks and libraries like PyTorch, Numpy, Pandas, SciPy, Scikit-Learn, LangChain, and Hugging Face Transformers

JOB RESPONSIBILITIES:

• Design, train, fine-tune, and deploy LLMs using prompt engineering and RAG techniques
• Build scalable solutions using frameworks and libraries like PyTorch, Hugging Face Transformers, and LangChain
• Collaborate with cross-functional teams to deliver data-driven solutions
• Develop efficient data pipelines using big data technologies like Spark and Hadoop
• Ensure compliance with data privacy and ethical AI practices

REQUIRED SKILLS:

1. Strong proficiency in Python with working knowledge of Java, Scala, or C++
2. Experience with LLMs, including training and deployment
3. Prompt engineering and RAG techniques
4. Frameworks and libraries like PyTorch, Numpy, Pandas, SciPy, Scikit-Learn, LangChain, and Hugging Face Transformers
5. SQL and big data technologies like Hadoop and Spark
6. Cloud platforms like AWS, Azure, or Google Cloud

IDEAL CANDIDATE:

• Master’s or Ph.D. in Computer Science, Statistics, or related field
• 5+ years of experience in machine learning, deep learning, and statistical modeling
• Strong proficiency in Python and other programming languages
• Expertise in PyTorch, Hugging Face, LangChain, Scikit-Learn, Pandas, NumPy, and SciPy
• Strong analytical and problem-solving abilities
• Effective communication skills and ability to work in cross-functional teams

WHAT TO EXPECT:

• Remote working with flexible work arrangements
• Opportunity to work with a large-scale/global company