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.