AI Engineer (LLM / GenAI)
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AI Summary
Design and deploy scalable GenAI architectures and REST APIs using cloud environments and containerization. Develop AI agents and multi-agent systems while managing end-to-end MLOps pipelines for business use cases.
Key Responsibilities:
Solution Architecture & Deployment:
Solution Architecture & Deployment:
- Design and deploy secure, scalable GenAI architectures integrated into applications
- Build and deploy REST APIs for AI/ML models
- Work with Docker, Kubernetes in cloud environments (AWS/Azure/GCP)
- Fine-tune and optimize LLMs (GPT, VAEs, GANs, transformer-based models)
- Implement RAG pipelines, embedding, and prompt engineering techniques
- Work with commercial and open-source LLMs (GPT, Claude, LLaMA, Phi)
- Build and deploy AI agents using LangChain, LangGraph, CrewAI, Autogen, AgentFlow
- Implement multi-agent systems, orchestration, tool integration, and state management
- Develop autonomous or semi-autonomous workflows for business use cases
- Set up end-to-end MLOps pipelines (CI/CD, monitoring, retraining)
- Optimize performance, scalability, and infrastructure costs
- Use tools like Git, Docker, Kubernetes, vector databases
- Develop APIs using FastAPI / Node.js
- Work with React, TypeScript, async patterns, WebSockets/SSE
- Handle data integration using REST APIs, SQL, and external systems
- Partner with Engineering, Product, and Data teams
- Communicate complex AI concepts clearly to technical and non-technical stakeholders
- Stay updated with the latest advancements in GenAI and AI agents
- Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain)
- Hands-on experience with LLMs, RAG, embedding, and prompt tuning
- Experience building AI agents and multi-agent systems
- Experience with cloud platforms (AWS/Azure/GCP) and containerization
- Strong knowledge of REST APIs and data integration
- Experience with FastAPI, Node.js, React, TypeScript
- Understanding of MLOps and deployment practices
- Strong analytical, problem-solving, and communication skills
- 4+ years of experience with GenAI/LLMs in production
- Experience with agent orchestration frameworks (CrewAI, LangGraph, Autogen)
- Exposure to client-facing AI solutions or cross-functional projects
- Open-source contributions, research, or AI project portfolio
Requirements
- Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain)
- Hands-on experience with LLMs, RAG, embedding, and prompt tuning
- Experience building AI agents and multi-agent systems
- Experience with cloud platforms (AWS/Azure/GCP) and containerization
- Strong knowledge of REST APIs and data integration
- Experience with FastAPI, Node.js, React, TypeScript
- Understanding of MLOps and deployment practices
- Strong analytical, problem-solving, and communication skills
Benefits
- Competitive salary and performance-based bonuses.
- Comprehensive insurance plans.
- Collaborative and supportive work environment
- Chance to learn and grow with a talented team.
- A positive and fun work environment
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