AI Engineer

4+ years of hands-on experience with LLMs and GenAI in production settings. 1. Solution Architecture & Deployment Design and deploy scalable, secure GenAI architectures integrated into customer-facing products. Build REST APIs for AI/ML models and deploy them in containerized environments (Docker, Kubernetes) on cloud platforms (AWS, Azure, GCP). 2. GenAI & LLM Development Fine-tune and optimize generative models including GPT, VAEs, GANs, and transformer-based architectures. Apply techniques like Retrieval-Augmented Generation (RAG) and prompt engineering to enhance model performance and relevance. Work with both commercial and open-source LLMs (e.g., GPT- 4, Claude, LLaMA 3.2, Phi). 3. Agentic AI Integration Primary Focus: Build, deploy, and optimize AI agents leveraging frameworks such as LangChain, LangGraph, CrewAI, AgentFlow, and Autogen. Implement orchestration strategies, multi-agent collaboration, tool integration, and memory/state management. Drive experimentation to create autonomous or semi- autonomous agents that solve real business workflows and decision-making processes. 4. MLOps & Performance Optimization Establish MLOps pipelines covering model lifecycle: training, CI/CD, monitoring, and retraining. Use tools like Git, Docker, Kubernetes, and vector DBs to ensure efficient and reliable deployment. Optimize resource utilization and infrastructure costs. 5. Cross-Functional Collaboration Partner with engineering, data science, and product teams to align technical solutions with business goals. Effectively communicate complex concepts across diverse technical and non-technical audiences. Stay current with industry advancements and drive innovation in GenAI and AI agent strategy. 4+ years of hands-on experience with LLMs and GenAI in production settings. 1. Solution Architecture & Deployment Design and deploy scalable, secure GenAI architectures integrated into customer-facing products. Build REST APIs for AI/ML models and deploy them in containerized environments (Docker, Kubernetes) on cloud platforms (AWS, Azure, GCP). 2. GenAI & LLM Development Fine-tune and optimize generative models including GPT, VAEs, GANs, and transformer-based architectures. Apply techniques like Retrieval-Augmented Generation (RAG) and prompt engineering to enhance model performance and relevance. Work with both commercial and open-source LLMs (e.g., GPT- 4, Claude, LLaMA 3.2, Phi). 3. Agentic AI Integration Primary Focus: Build, deploy, and optimize AI agents leveraging frameworks such as LangChain, LangGraph, CrewAI, AgentFlow, and Autogen. Implement orchestration strategies, multi-agent collaboration, tool integration, and memory/state management. Drive experimentation to create autonomous or semi- autonomous agents that solve real business workflows and decision-making processes. 4. MLOps & Performance Optimization Establish MLOps pipelines covering model lifecycle: training, CI/CD, monitoring, and retraining. Use tools like Git, Docker, Kubernetes, and vector DBs to ensure efficient and reliable deployment. Optimize resource utilization and infrastructure costs. 5. Cross-Functional Collaboration Partner with engineering, data science, and product teams to align technical solutions with business goals. Effectively communicate complex concepts across diverse technical and non-technical audiences. Stay current with industry advancements and drive innovation in GenAI and AI agent strategy.