Lead Gen AI Engineer

fluid.live; Chennai District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

fluid.live seeks a GenAI Lead Engineer to spearhead design, development, and deployment of advanced Generative AI solutions. You will own LLM orchestration, multimodal systems, and enterprise-scale deployment, driving production-ready AI with strong leadership.

You will architect LLM pipelines, manage RAG systems, vector stores, and CI/CD, while collaborating across teams to deliver scalable AI products. Expertise in Python, PyTorch and cloud-native MLOps is essential.

Qualifications

• 6-12 years in AI/ML engineering with 3+ years in GenAI/LLM systems.
• Strong programming in Python with deep ML framework experience.
• Hands-on with LangChain, Hugging Face and LlamaIndex in production.

Responsibilities

• Architect and deploy LLMs, diffusion models, and multimodal AI.
• Lead CI/CD, monitoring, drift detection and automated testing in prod.
• Develop scalable RAG pipelines and manage vector databases.
• Integrate AI models via RESTful APIs, GraphQL and Kafka.
• Champion enterprise MLOps and cloud deployments across multiple clouds.

Skills

Python

PyTorch

TensorFlow

LangChain

Hugging Face

LLM orchestration

Tools

vLLM

Ollama

quantization

Docker

Kubernetes

RESTful APIs

Kafka

Job description

About the Role

We are seeking a highly skilled GenAI Lead Engineer to spearhead the design, development, and deployment of advanced Generative AI solutions. The ideal candidate will have deep expertise in LLM orchestration, multimodal AI systems, and enterprise-scale deployment, with proven leadership in delivering production-ready AI solutions.

Key Responsibilities

• Multimodal AI Deployment: Architect and deploy LLMs, diffusion models, and multimodal AI. Optimize inference using vLLM, Ollama, quantization.
• LLM Orchestration: Build pipelines with LangChain, Hugging Face, LlamaIndex, AutoGen. Integrate APIs from OpenAI, Anthropic, Mistral.
• RAG Systems: Design chunking strategies, embeddings, cross-encoding, hybrid search. Build scalable RAG pipelines.
• Vector Databases: Manage Pinecone, Weaviate, ChromaDB, FAISS, Milvus for semantic search.
• Enterprise MLOps: Lead CI/CD pipelines, model monitoring, drift detection, automated testing.
• Prompt Engineering: Apply LoRA, QLoRA, PEFT methods. Develop reusable prompt libraries.
• Systems Integration: Integrate AI models via RESTful APIs, GraphQL, Kafka.
• Cloud Deployment: Deploy workloads across AWS, GCP, Azure. Containerize with Docker/Kubernetes, configure serverless endpoints.

Qualifications

• 6-12 years in AI/ML engineering, with 3+ years in GenAI/LLM systems.
• Strong expertise in Python, PyTorch, TensorFlow.
• Hands-on experience with LangChain, Hugging Face, LlamaIndex.
• Proven track record of deploying LLMs/multimodal models in production.
• Cloud-native architecture and MLOps experience.
• Excellent problem-solving, leadership, and communication skills.

Preferred Skills

• Enterprise-scale RAG systems.
• Vector database optimization.
• Multi-cloud deployments.
• Strong background in data preprocessing, evaluation, monitoring pipelines.