Artificial Intelligence Engineer

We're looking for a skilled AI Engineer to build and manage our end-to-end AI pipelines and support the team migration to an AI agents based development. You'll focus on Retrieval-Augmented Generation (RAG) integration and Large Language Model (LLM) deployment across on-premise infrastructure and AWS Bedrock.

Key Responsibilities:

• Develop & Manage AI Pipelines: Design, build, and maintain robust AI/ML pipelines for data, training, and deployment. Implement MLOps best practices (CI/CD, monitoring).
• AWS Bedrock Expertise: Architect and implement AI solutions using AWS Bedrock services, including foundational models and custom model integration.
• RAG Integration: Design, develop, and deploy RAG systems to enhance LLMs with external knowledge bases. Optimize vector databases and retrieval mechanisms.
• LLM Management (On-Prem & Bedrock): Handle the full lifecycle of LLMs, from selection and fine-tuning to deployment and monitoring, across both on-premise (GPU, Docker, Kubernetes) and Bedrock environments. Ensure security and optimize performance.
• Collaboration: Work as a part of the R&D team, work closely with the product teams, providing technical guidance and documenting solutions.
• Team enhancement: Support the team in migration to agentic development life cycle

Qualifications:

• Bachelor's or Master's in Computer Science, AI, Data Science, or related field.
• 3+ years in AI/ML engineering, building production-grade solutions.
• Hands-on experience with LLMs and RAG architectures.
• Proficiency in Python and AI/ML frameworks (TensorFlow, PyTorch, Hugging Face, LangChain).
• Extensive experience with AWS services, especially Bedrock, SageMaker, S3.
• Experience with on-premise LLM deployment (GPU infrastructure, Docker, Kubernetes).
• Strong understanding of MLOps principles and tools (MLflow, Kubeflow).
• Experience with vector databases (Pinecone, Weaviate) and embedding models.
• Solid software engineering practices (Git, testing)