AI/ML Engineer

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 :

- 5+ 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.