Gen AI Engineer

Role Overview

We are seeking a skilled Generative AI Engineer to design, develop, and deploy AI solutions using Large Language Models (LLMs), multimodal models, vector databases, and AI agents. The ideal candidate should have hands-on experience in building AI-powered applications, optimizing model performance, integrating AI systems into production environments, and staying current with the rapidly evolving GenAI landscape.

Key Responsibilities

• Design, develop, and deploy GenAI-based solutions using LLMs (e.g., GPT, Llama, Claude, Mistral).
• Build and fine-tune foundation models for application-specific tasks such as:
  
  • Text generation and classification
  • Autonomous AI agents
  • Retrieval-Augmented Generation (RAG)
  • Knowledge Augmented Generation (KAG)
  • Chatbots and conversational systems
    
• Integrate AI models with applications using frameworks such as Lang Chain and Lang graph.
• Develop scalable inference architectures using GPU/CPU compute systems, vector databases, and orchestration frameworks.
• Implement RAG/KAG pipelines using vector stores like Pinecone or Weaviate.
• Optimize model performance, latency, and token cost.
• Work with prompt engineering, evaluation strategies, and quality measurement frameworks.
• Deploy AI workloads to cloud platforms (AWS, Azure, GCP) using services such as:
  
  • Bedrock, Sage maker, Vertex AI, Azure OpenAI
  • Kubernetes, Docker
    
• Implement data pipelines and manage training datasets for continual improvement.
• Ensure compliance with responsible AI practices, including security, privacy, and hallucination reduction.
• Collaborate with product, UX, and engineering teams to deliver customer-focused AI solutions.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, Engineering, or equivalent.
• Strong programming skills in Python.
• Hands-on experience with:
  
  • LLMs and NLP systems
  • KAG and RAG implementations
  • Embeddings, vector search, and knowledge indexing
    
• Experience with AI orchestration tools:
  
  • Lang Chain, Lang graph, or similar
    
• Familiarity with GPU compute optimization.
• Strong understanding of REST APIs, microservices, and modern backend architecture.
• Experience with cloud platforms such as AWS, GCP, or Azure