Generative AI Consultant

Role Overview

Key Responsibilities

• Architect and implement LLM-based agent frameworks to support task automation and intelligent decision-making.
• Develop and optimize RAG (Retrieval-Augmented Generation) systems using efficient chunking strategies for context-aware responses.
• Use LangChain to orchestrate modular GenAI workflows integrated with vector databases (e.g., FAISS, Pinecone, Chroma).
• Build and maintain knowledge graphs and incorporate Vision APIs for multimodal intelligence.
• Apply advanced prompt engineering and token management to control costs and improve LLM accuracy.
• Ensure reliability and robustness through hallucination control methodologies and responsible AI practices.
• Design and deploy scalable GenAI applications on cloud platforms (Azure, AWS, or GCP).
• Collaborate with cross-functional teams including data scientists, cloud engineers, and domain experts to integrate GenAI into products.
• Leverage tools like Docker for containerization and Git for code versioning.
• Use SQL and Python to process and manipulate data effectively.
  

Must-Have Skills

• Agent Frameworks (e.g., LangChain Agents)
• Retrieval-Augmented Generation (RAG)
• Chunking Strategies for document processing
• Deep understanding of LLMs (e.g., OpenAI, Anthropic, Meta, Cohere)
• Experience with AI services on Azure, AWS, or GCP (at least one)
• LangChain framework
• Vector Databases (e.g., FAISS, Pinecone, Weaviate)
• Token Management strategies
• Knowledge Graphs (e.g., Neo4j, RDF-based models)
• Vision APIs (e.g., Google Vision, AWS Rekognition, Azure Computer Vision)
• Prompt Engineering expertise
• Proficiency in Python
  

Good-to-Have Skills

• AI/ML Algorithms and optimization
• Deep Learning frameworks (e.g., PyTorch, TensorFlow)
• Computer Vision models and techniques
• Hallucination Control Techniques
• Responsible AI frameworks and compliance methodologies
  

Technical Proficiencies

• Programming : Python (mandatory)
• Cloud : Azure, AWS, or GCP (at least one is mandatory)
• Containers : Docker
• Database : SQL
• Version Control : Git or similar
  

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Relevant certifications in AI/ML or Cloud Platforms are a plus.
• Strong analytical thinking, problem-solving, and communication skills.