AI/GenAI Engineer

Position: AI/GenAI Engineer (LLM Integration Specialist)

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)

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

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