AI Engineer – LLMs , GenAI

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.
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.