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.