AI Software Engineer
AI SSE
Department: Tech
Experience: 4+
- Design, develop, and deploy production-ready AI applications powered by Large Language Models (LLMs).
- Build and maintain agentic AI workflows using orchestration frameworks.
- Develop prompt engineering strategies and optimize AI model performance.
- Design and implement Retrieval-Augmented Generation (RAG) pipelines and memory architectures.
- Build multi-agent systems for planning, execution, validation, and decision-making.
- Integrate AI agents with internal and external tools using APIs and function calling.
- Implement monitoring, evaluation, and observability for AI applications.
- Optimize AI systems for latency, reliability, scalability, and inference costs.
- Troubleshoot production issues related to LLM behavior, hallucinations, tool failures, and model performance.
- Collaborate with cross-functional teams to translate business requirements into AI-powered solutions.
- Stay up to date with the latest advancements in Generative AI, Agentic AI, and LLM technologies.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 4–8 years of software engineering experience.
- Minimum 2 years of hands-on experience building AI/LLM-based applications.
- Strong programming skills in Python or TypeScript.
- Experience working with LLMs such as OpenAI GPT, Claude, Gemini, or similar models.
- Strong understanding of Prompt Engineering and AI application development.
- Experience building AI agent workflows using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar.
- Hands-on experience with Retrieval-Augmented Generation (RAG), vector databases, and embedding models.
- Experience integrating APIs, tools, and external services with AI systems.
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.
- Familiarity with Docker, Kubernetes, CI/CD pipelines, and Git.
- Strong problem-solving and debugging skills.
- Experience building production-scale multi-agent AI systems.
- Knowledge of AI evaluation frameworks and LLM testing methodologies.
- Experience with observability tools for AI applications.
- Understanding of model routing, fine-tuning, and inference optimization.
- Experience with distributed systems and microservices architecture.
- Contributions to open-source AI projects or personal AI applications are a plus.
- Opportunity to work on cutting-edge Agentic AI products.
- Collaborative engineering culture with high ownership.
- Exposure to production-scale AI systems.
- Fast-paced environment with opportunities to learn and grow.
- Competitive compensation and benefits.