AI/GenAI Engineer

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.

Required Skills & Experience

Must Have:

• Strong hands-on experience building chatbots / conversational AI systems / AI

Agents with LLMs and Generative AI technologies, including OpenAI, Anthropic

Claude, and other major LLM APIs.

• Experience in Python frameworks such as FastAPI, LangChain, and LlamaIndex

for building scalable LLM-powered applications.

• Experience in prompt engineering, chain-of-thought prompting, ReAct, and agent-

based architectures.

• Proven expertise in RAG (Retrieval-Augmented Generation) systems using vector

databases such as Pinecone, Weaviate, Qdrant, and ChromaDB.

• Familiarity with transformer architecture, attention mechanisms, and embedding

models for semantic search and context retrieval.

• Experienced in fine-tuning and parameter-efficient adaptation (LoRA, QLoRA,

PEFT), model quantisation, and optimisation for cost-effective inference.

• Practical knowledge of open-source models (Llama, Mistral, Falcon) and

deployment using vLLM or TGI (Text Generation Inference).

• Skilled in API integration, webhooks, streaming responses, and function

calling/tool use with LLMs.

• Experience in model evaluation using BLEU, ROUGE, BERTScore, and similar

metrics for performance benchmarking.

• Proficient in MLOps, including experiment tracking (Weights & Biases, MLflow)

and deployment pipelines on AWS SageMaker, Google Vertex AI, or Azure ML.

• Exposure to multi-modal models (vision, audio) and integration into unified AI

workflows.

• Strong problem-solving, debugging, and optimization skills, including token cost

analysis and inference efficiency.

• Contributions to the AI/ML community, including open-source or research-

oriented publications.

Tech Stack: