SMARTnCODE - Generative AI Engineer/LLM Integration Specialist

Position : AI/GenAI Engineer (LLM Integration Specialist)

Experience : 2 - 4 years

CTC : 12 to 16 LPA

Location : Hyderabad

About the Role :

We're building a high-performance chat application and looking for an AI/GenAI Engineer to lead the integration and optimization of Large Language Models (LLMs). You'll be responsible for connecting LLM APIs, implementing domain-specific fine-tuning strategies, prompt engineering, and ensuring optimal performance for production use.

Key Responsibilities :

LLM Integration & Architecture :

- Integrate multiple LLM APIs (OpenAI, Anthropic Claude, Google Gemini, or open-source models)

- Design and implement robust API wrapper services with retry logic, fallback mechanisms, and error handling

- Implement streaming responses for real-time chat experience

- Build rate limiting and quota management systems

- Handle token counting, context window management, and cost optimization

Domain Customization & Fine-tuning :

- Develop domain-specific prompt engineering strategies

- Implement RAG (Retrieval Augmented Generation) pipelines using vector databases

- Fine-tune or adapt models for specific use cases using techniques like LoRA, prompt tuning

- Create and maintain knowledge bases for domain-specific responses

Performance & Optimization :

- Optimize API response times and reduce latency

- Implement caching strategies for common queries

- Monitor and optimize token usage to control costs

Safety & Quality :

- Implement content moderation and safety filters

- Build guardrails to prevent prompt injection and jailbreaking

- Develop evaluation frameworks to measure response quality

Technical Stack :

- Languages : Python (primary), JavaScript/TypeScript (basic understanding)

- LLM APIs : OpenAI, Anthropic, Google Gemini, Cohere

- Frameworks : LangChain, LlamaIndex, FastAPI

- Vector DBs : Pinecone, Weaviate, Qdrant, or ChromaDB

- Infrastructure : Docker, Redis, PostgreSQL, Message Queues

- Cloud : AWS/GCP/Azure

Infrastructure :

- Design scalable architecture for handling concurrent LLM requests

- Implement queue systems for managing high-volume API calls

- Set up monitoring and logging for LLM interactions

- Work with DevOps to deploy models (if self-hosted)

Required Skills & Experience :

Must Have :

- Experience working with LLMs and GenAI technologies

- Strong experience with OpenAI API, Anthropic Claude, or similar LLM APIs

- Proficiency in Python (FastAPI, LangChain, LlamaIndex preferred)

- Strong understanding of prompt engineering techniques and best practices

- Experience with vector databases (Pinecone, Weaviate, Qdrant, ChromaDB)

- Knowledge of RAG (Retrieval Augmented Generation) implementation

- Understanding of transformer architecture and attention mechanisms

- Experience with API integration, webhooks, and streaming responses

- Strong problem-solving skills and ability to debug complex AI systems

- Experience with LangChain, LlamaIndex, or similar LLM frameworks

- Knowledge of fine-tuning techniques (LoRA, QLoRA, PEFT)

- Experience with embedding models and semantic search

- Familiarity with HuggingFace Transformers library

- Experience deploying models using vLLM, TGI (Text Generation Inference)

- Knowledge of function calling/tool use with LLMs

- Experience with model evaluation metrics (BLEU, ROUGE, BERTScore)

- Understanding of token economics and cost optimisation

- Experience with open-source models (Llama, Mistral, Falcon)

- Knowledge of model quantization and optimization techniques

- Experience with multi-modal models (vision, audio)

- Familiarity with MLOps practices and experiment tracking (Weights & Biases, MLflow)

- Experience with AWS SageMaker, Google Vertex AI, or Azure ML

- Understanding of chain-of-thought prompting, ReAct, agents

- Experience building chatbots or conversational AI systems

- Publications or contributions to AI/ML community