Job Summary
Role Summary
The AI Engineer will design, build, deploy, and operate production-grade Large Language Model (LLM) solutions. This role focuses on engineering rigor—turning LLM capabilities into reliable, scalable, and secure enterprise applications, and continuously improving them based on performance, cost, and user feedback.
Key Responsibilities
Key Responsibilities
• Design and build LLM-powered applications using proprietary and open-source models.
• Implement prompt engineering, Retrieval-Augmented Generation (RAG), tool/function calling, and agent workflows.
• Deploy LLM solutions into cloud and enterprise environments with scalability and reliability.
• Build inference APIs, microservices, and CI/CD pipelines for AI applications.
• Monitor model quality, latency, cost, drift, and hallucinations in production.
• Fine-tune and enhance models using parameter-efficient techniques where required.
• Optimize inference performance using caching, batching, quantization, and prompt optimization.
• Ensure security, privacy, and responsible AI guardrails in all deployments.
• Collaborate with product, platform, and engineering teams to deliver enterprise AI solutions.
Skill Requirements
Required Skills & Experience
• Strong programming skills in Python; experience with backend APIs.
• Hands-on experience with LLM frameworks (LangChain, LlamaIndex, or equivalent).
• Experience building RAG pipelines using vector databases.
• Knowledge of Docker, Kubernetes, cloud platforms, and CI/CD pipelines.
• Familiarity with LLMOps / MLOps tools and monitoring systems.
Other Requirements
Experience Level
• 3–5 years of overall software or ML engineering experience.
• 1–3+ years of hands-on experience delivering LLM or Generative AI solutions in production.
Success Expectations
• Production-grade LLM solutions running reliably at scale.
• Continuous improvement in quality, cost-effectiveness, and user outcomes.
• Reusable AI engineering patterns that accelerate enterprise AI adoption.
