Description

You will design and deploy scalable Generative AI architectures on AWS, Azure, and GCP.

This role is on-site.

Responsibilities

• Architect secure AI platforms integrating LLMs and diffusion models into enterprise workflows.
• Lead model fine-tuning and prompt engineering using custom datasets for domain-specific applications.
• Implement MLOps best practices, including CI/CD pipelines, model versioning, monitoring, and automated deployments.
• Optimize inference pipelines for low latency, high throughput, and cost efficiency.
• Collaborate with data scientists and product managers to ensure compliance with AI ethics and data privacy standards.

Required Skills

• 10+ years of overall experience in software, data, or machine learning engineering.
• 5+ years of hands-on experience in Machine Learning or AI.
• Strong expertise in Generative AI, LLMs, and diffusion models.
• Proficiency in Python programming and scripting.
• Deep knowledge of ML frameworks: TensorFlow, PyTorch, Hugging Face Transformers, and OpenAI APIs.
• Strong experience deploying AI solutions on AWS, Azure, and GCP.
• Understanding of vector databases (Pinecone, Weaviate, or Qdrant) and orchestration frameworks (LangChain, LlamaIndex).

Preferred Skills

• Experience with knowledge graphs, embeddings, and semantic search.
• Contributions to open-source projects, research publications, or GenAI community engagement.

Key Skills

Education

Any Graduate