Generative AI Engineer - (5+ Years Experience)

Our client is looking for a highly skilled Generative AI Engineer (5+ years) with strong expertise in LLM development, RAG architectures, vector databases, and scalable AI systems. You will play a key role in building, optimizing, and deploying enterprise-grade AI solutions across diverse business use cases.

This role is ideal for someone who has hands-on experience developing real-world AI applications and is ready to take ownership of end-to-end AI pipelines, model performance, and technical decision-making.

• Design, architect, and develop advanced Generative AI and LLM-powered applications (chatbots, copilots, agents, workflow automation, synthetic data systems, etc.).
• Build and optimize RAG pipelines including embeddings, chunking strategies, vector search, and ranking layers.
• Implement vector search systems using Pinecone, Weaviate, FAISS, Qdrant, or Chroma.
• Develop and integrate LLM models into production systems using Python (FastAPI/Flask) or Node.js.
• Design and implement evaluation frameworks to test prompt quality, reduce hallucinations, and improve reliability.
• Perform model fine-tuning, parameter-efficient training (LoRA/QLoRA), and prompt optimization for domain-specific tasks.
• Lead AI model orchestration and workflow automation using frameworks like LangChain, LangGraph, LlamaIndex, or CrewAI.
• Work with cross-functional teams to understand business requirements and convert them into scalable AI solutions.
• Deploy AI services on cloud platforms (AWS, GCP, or Azure) with proper CI/CD, MLOps, and monitoring best practices.
• Conduct performance tuning, cost optimization, and scalability improvements.
• Mentor junior AI engineers and contribute to internal best practices and knowledge-sharing.

• 5+ years of professional experience in AI/ML, NLP, or backend engineering with at least 5+ years in Generative AI / LLM-focused roles.
• Strong proficiency in Python, including FastAPI/Flask for API development.
• Deep understanding of LLMs, embeddings, tokenization, transformers, and retrieval systems.
• Experience designing and scaling RAG systems end-to-end.
• Hands-on expertise with vector databases (Pinecone, FAISS, Weaviate, Qdrant, Chroma).
• Strong experience working with AI APIs (OpenAI, Anthropic, Gemini, HuggingFace, Cohere, etc.).
• Solid understanding of prompt engineering, context optimization, and evaluation frameworks.
• Experience with fine-tuning and parameter-efficient training (LoRA, QLoRA).
• Strong foundation in backend development, REST APIs, microservices, and distributed systems.
• Experience with Docker, cloud platforms (AWS/GCP/Azure), and basic MLOps tooling.
• Familiarity with asynchronous job queues, caching layers, and scalable system design.

• Experience with multimodal AI (image, audio, video models).
• Hands-on experience with agent frameworks (LangGraph, CrewAI, AutoGen).
• Knowledge of retrieval compression (colBERT, late interaction models, hybrid search).
• Familiarity with Kubernetes or container orchestration.
• Experience building internal tools, copilots, or domain-specific AI assistants.
• Exposure to security & compliance for AI systems (PII handling, data governance).

• Strong problem-solving and analytical mindset with a product-oriented approach.
• Ability to communicate complex technical concepts to stakeholders.
• Ownership mindset able to drive projects independently.
• Ability to work in a fast-paced, dynamic environment.
• Mentorship capability for guiding junior team members.
• Continuous learner with curiosity for new AI advancements.

#GenerativeAI #LLMEngineer #AIDeveloper #AIEngineer #OpenAI #HuggingFace #Transformers #LangChain #LangGraph #RAGSystems #VectorSearch #Pinecone #Qdrant #MachineLearningEngineer #PythonDeveloper #MLOps #FineTuning #LoRA #QLoRA #AIAutomation