Description

You will design and deploy production-grade Generative AI and LLM-based systems, owning the full lifecycle from architecture to monitoring.

Responsibilities

• Build and optimize RAG pipelines using structured and unstructured data, leveraging vector databases like Pinecone, Weaviate, or Azure AI Search.
• Develop LLM applications using orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel, implementing prompt engineering and tool use.
• Architect scalable backend systems and APIs in Python, ensuring reliability and performance in distributed environments.
• Implement evaluation, testing, and monitoring strategies for AI systems to ensure accuracy and safety.
• Containerize and deploy services using Docker and Kubernetes via CI/CD pipelines on AWS, GCP, or Azure.

Required Skills

• 8+ years of software engineering, machine learning, or applied AI experience.
• 2+ years of hands-on experience building and deploying Generative AI / LLM systems in production.
• Strong Python programming skills with experience in backend and API development.
• Proficiency with vector databases (Pinecone, Weaviate, Chroma, FAISS, Elasticsearch) and retrieval systems.
• Experience with orchestration frameworks: LangChain, LlamaIndex, or Semantic Kernel.
• Familiarity with generative AI techniques including GANs and VAEs.
• Cloud platform experience (AWS, GCP, or Azure) and production deployment practices.
• Understanding of software architecture, scalability, and distributed systems.

Preferred Skills

• Experience fine-tuning or adapting open-source LLMs and advanced NLP for text generation.
• Experience with MLOps tools (MLflow, SageMaker, Vertex AI, Kubeflow) and multi-agent systems.

Key Skills

Education

Any Graduate