Description

Key Skills: LLM, Large Language Model, Generative AI, NLP, Retrieval-Augmented Generation (RAG)

Roles and Responsibilities:

• Design and develop Generative AI and LLM-based applications for business use cases.
• Work with LLMs, foundation models, embeddings, and NLP technologies.
• Build and implement RAG (Retrieval-Augmented Generation) solutions.
• Develop effective prompt engineering strategies using zero-shot and few-shot prompting and reusable prompt templates.
• Optimize prompts and retrieval strategies to improve accuracy and response quality.
• Develop and integrate AI agents and LLM-powered services.
• Evaluate GenAI applications and agents based on quality, accuracy, effectiveness, and performance.
• Work with vector databases and embedding models for information retrieval.
• Deploy GenAI applications using AWS, Azure, or GCP.
• Build scalable and highly available GenAI solutions for production environments.
• Implement MLOps/LLMOps practices including CI/CD, monitoring, logging, and version control.
• Develop APIs and backend services to integrate GenAI solutions with enterprise applications.
• Troubleshoot and optimize GenAI applications for performance, scalability, cost, and reliability.
• Work closely with business, data science, software engineering, and cloud teams.
• Stay updated with the latest developments in Generative AI, LLMs, RAG, AI agents, and NLP.

Skills Required:

• 4-10 years of experience in AI/ML, Generative AI, or related technologies.
• Strong understanding of Generative AI and Large Language Models (LLMs).
• Good knowledge of NLP fundamentals.
• Hands-on experience with RAG architecture and implementation.
• Strong knowledge of prompt engineering.
• Experience working with embeddings and vector databases.
• Experience in developing LLM-powered applications and AI agents.
• Knowledge of GenAI/agent evaluation and benchmarking.
• Experience with at least one cloud platform: AWS, Azure, or GCP.
• Strong programming skills in Python, Java, or .NET.
• Knowledge of AI/ML frameworks such as PyTorch, TensorFlow, or Keras.
• Understanding of MLOps/LLMOps, CI/CD, monitoring, and logging.
• Good understanding of APIs, application integration, and software development practices.
• Strong analytical, problem-solving, and communication skills.

Preferred Skills

• Experience with OpenAI, Azure OpenAI, Gemini, Claude, or other LLM platforms.
• Knowledge of LangChain, LlamaIndex, or similar GenAI frameworks.
• Experience with FAISS, Pinecone, Chroma, Weaviate, Elasticsearch, or other vector databases.
• Knowledge of AI Agent frameworks and tool/function calling.
• Experience deploying GenAI applications in production environments.
• Understanding of GenAI security, data privacy, and responsible AI practices.
• Relevant AI/ML or Generative AI certifications will be an advantage.

Education: Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Machine Learning, Engineering, or a related field

Key Skills

Education

Any Graduate