Role & responsibilities

About the Role

We're building a high-performance chat application 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.

You'll work closely with our React and Python developers to create a seamless, intelligent chat experience that serves thousands of concurrent users.

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
- Implement few-shot learning and in-context learning strategies

Performance & Optimization

- Optimize API response times and reduce latency
- Implement caching strategies for common queries
- Monitor and optimize token usage to control costs
- A/B test different models and prompts for quality improvements




- Build fallback chains (primary/secondary model routing)

Safety & Quality

- Implement content moderation and safety filters
- Build guardrails to prevent prompt injection and jailbreaking
- Develop evaluation frameworks to measure response quality
- Monitor and handle hallucinations and inaccuracies
- Implement user feedback loops for continuous improvement

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)

Preferred candidate profile

Must Have:

- Experience working with LLMs and GenAI technologies
- Robust 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 x 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

Technical Stack You'll Work With

- 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 (whatever your team uses)
- Monitoring: Prometheus, Grafana, custom LLM analytics

📌 AI/GenAI Engineer (LLM Integration Specialist) (Hyderabad)
🏢 Smartncode
📍 Hyderabad