Generative AI Engineer
1 week ago
Roles and Responsibilities :
- Develop and fine-tune LLMs using techniques like RAG, transfer learning, or domain-specific adaptation
- Build AI agents using frameworks like LangChain or CrewAI to manage dynamic and multi-step workflows
- Work with vector databases (e.g., Pinecone) to enable semantic search and retrieval
- Design and maintain ETL pipelines and ensure smooth data preprocessing and transformation
- Implement NLP solutions for tasks like intent detection, sentiment analysis, and content generation
- Develop backend APIs and services using Python frameworks like FastAPI or Flask
- Contribute to scalable microservice-based architectures
Requirements :
- Bachelor's degree in Computer Science, Information Technology, or a related field.
- 2 to 4 years in AI/ML development and backend system
- Machine Learning Fundamentals : Strong grasp of algorithms, model training, evaluation, and tuning
- Generative AI Models : Experience working with LLMs, RAG architecture, and fine tuning techniques
- LangChain or Similar Frameworks : Hands-on experience building AI workflows using toolkits like LangChain.
- Natural Language Processing (NLP) : Proficiency in text analytics, classification, tokenization, embeddings.
- Vector Databases : Practical use of tools like Pinecone, FAISS, or similar for retrieval-augmented generation
- Big Data Handling : Ability to work with large datasets, optimize storage, and processing pipelines
- SQL/NoSQL : Experience in querying and managing structured and unstructured data
- Python & API Development : Proficiency in Python and frameworks like FastAPI or Flask
- ETL & Data Preprocessing : Strong understanding of building pipelines for clean and efficient data processing
- Soft Skills : Strong problem-solving, communication, and collaboration abilities.
Good-to-Have Skills :
- Agentic AI Tools : Exposure to CrewAI or similar platforms for orchestrating multi agent interactions.
- Content Structuring : Experience in clustering, topic modeling, or organizing unstructured data
- ETL Enhancements : Advanced optimization techniques for faster and more efficient pipelines
- Domain Exposure : Prior work on projects involving customer insights, chat summarization, or sentiment analysis
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