AI/ML Engineer
2 days ago
Key Responsibilities :
- Lead the development and implementation of AI/ML models, specializing in deep learning techniques including supervised, unsupervised, self-supervised, and reinforcement learning.
- Architect and deploy solutions using Large Language Models (LLMs) including transformers, self-attention mechanisms, mixture of experts, and embeddings.
- Design and implement Retrieval Augmented Generation (RAG) systems integrating vector databases, graph databases, and cutting-edge prompt engineering techniques.
- Develop and optimize AI agents, including orchestration and performance tuning for complex workflows.
- Perform model fine-tuning, data pre-processing, and feature engineering to improve AI system accuracy and efficiency.
- Utilize ML frameworks such as PyTorch, TensorFlow, or equivalent for model development and experimentation.
- Work with AI/ML tooling such as LangChain, LangGraph (preferred), CrewAI, LlamaIndex, and LLMOps platforms like LangFuse (preferred) or LangSmith.
- Deploy AI/ML models and applications on AWS, leveraging services such as ECS, Lambda, S3, and AI/ML platforms like SageMaker and Bedrock.
- Employ containerization and orchestration technologies including Docker and Kubernetes for scalable and reliable AI deployments.
- Collaborate closely with cross-functional teams to deliver end-to-end AI solutions focused on reliability, scalability, and usability in production environments.
- Apply strong problem-solving skills to troubleshoot and resolve challenges throughout the AI model lifecycle.
Required Qualifications and Skills :
years of professional experience in AI/ML with a focus on deep learning and large language models.
- Proven expertise in Retrieval Augmented Generation (RAG), vector and graph databases, and prompt engineering.
- Hands-on programming skills in Python and proficiency with ML frameworks like PyTorch or TensorFlow.
- Experience with AI/ML orchestration tools and platforms including LangChain, LangGraph, CrewAI, LlamaIndex, and LLMOps tools such as LangFuse or LangSmith.
- Strong knowledge of AWS cloud services and platforms for AI/ML deployment (ECS, Lambda, S3, SageMaker, Bedrock).
- Familiarity with containerization and orchestration tools like Docker and Kubernetes.
- Demonstrated ability to deploy scalable, production-grade AI/ML solutions with a focus on performance and user experience.
- Excellent communication, collaboration, and problem-solving skills.
Preferred Qualifications :
- Experience leading AI/ML teams or projects.
- Prior involvement in building AI-powered applications in domains such as NLP, conversational AI, or recommendation systems.
- Understanding of security, compliance, and ethical considerations in AI deployment.
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