AI Engineer - Autonomous Agents & Model Infrastructure

As an experienced AI Engineer, you will be responsible for designing, deploying, and managing autonomous agent systems on proprietary infrastructure. Your role will involve owning the full lifecycle of the process, from optimizing model weights to building production-grade agents with fine-tuning and reinforcement learning on on-premises or private cloud environments. Key Responsibilities: - Design and deploy autonomous agent architectures on AWS VPC and on-premise environments - Manage model weights and optimize for inference; implement LoRA and QLoRA fine-tuning for domain-specific tasks - Develop reinforcement learning pipelines for agent training with reward modeling and policy optimization - Implement MLOps/LLMOps infrastructure: model versioning, A/B testing, rollbacks, and evaluation frameworks - Architect RAG systems integrating vector databases with proprietary and fine-tuned models - Optimize model serving infrastructure (vLLM, TorchServe, TensorRT) for production inference - Build monitoring and observability systems for agent behavior and RL training quality - Ensure model security, data privacy, and audit compliance in enterprise deployments Required Qualifications: - 3-4 years hands-on experience in AI/ML/Data Science with at least 2 projects shipped to production - 2+ years dedicated experience in MLOps, LLMOps, or AIops (model deployment, inference optimization, pipeline automation, model management) - AWS proficiency across AI services: EC2, VPC, S3, IAM, SageMaker, Bedrock, Lambda, or custom ML infrastructure - Strong software engineering fundamentals: containerization (Docker), orchestration (Kubernetes), CI/CD, and API design - Hands-on experience deploying and serving large language models or foundation models in production environments - Practical experience with LoRA and QLoRA fine-tuning techniques for efficient model adaptation - Understanding of reinforcement learning fundamentals and experience implementing RL-based training - Working knowledge of vector databases and RAG implementation - Solid understanding of model optimization techniques and inference constraints Preferred Qualifications: - Experience building autonomous agents with RL frameworks and fine-tuning frameworks - QLoRA experience on consumer-grade GPUs in memory-constrained environments - Migration experience from cloud APIs to self-hosted models - On-premises or VPC-only deployment experience - Familiarity with agent frameworks and MLOps tools - Strong debugging and systems thinking approach with evidence-based problem-solving The company values pragmatic engineers who balance performance and cost, strong debuggers with evidence-based approaches, clear communicators, and owners who see projects end-to-end. You will work with technologies such as AWS, Python, Docker, Kubernetes, LLMs/Foundation Models, LoRA/QLoRA Libraries, Reinforcement Learning Frameworks, Vector Databases, Agent Frameworks, Model Serving Technologies, Fine-tuning Tools, and CI/CD Pipelines. As an experienced AI Engineer, you will be responsible for designing, deploying, and managing autonomous agent systems on proprietary infrastructure. Your role will involve owning the full lifecycle of the process, from optimizing model weights to building production-grade agents with fine-tuning and reinforcement learning on on-premises or private cloud environments. Key Responsibilities: - Design and deploy autonomous agent architectures on AWS VPC and on-premise environments - Manage model weights and optimize for inference; implement LoRA and QLoRA fine-tuning for domain-specific tasks - Develop reinforcement learning pipelines for agent training with reward modeling and policy optimization - Implement MLOps/LLMOps infrastructure: model versioning, A/B testing, rollbacks, and evaluation frameworks - Architect RAG systems integrating vector databases with proprietary and fine-tuned models - Optimize model serving infrastructure (vLLM, TorchServe, TensorRT) for production inference - Build monitoring and observability systems for agent behavior and RL training quality - Ensure model security, data privacy, and audit compliance in enterprise deployments Required Qualifications: - 3-4 years hands-on experience in AI/ML/Data Science with at least 2 projects shipped to production - 2+ years dedicated experience in MLOps, LLMOps, or AIops (model deployment, inference optimization, pipeline automation, model management) - AWS proficiency across AI services: EC2, VPC, S3, IAM, SageMaker, Bedrock, Lambda, or custom ML infrastructure - Strong software engineering fundamentals: containerization (Docker), orchestration (Kubernetes), CI/CD, and API design - Hands-on experience deploying and serving large language models or foundation models in production environments - Practical experience with LoRA and QLoRA fine-tuning techniques for efficient model adaptation - Understanding of reinforcement learning fundamentals and experience implementing RL-based training