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About the Role We are seeking an experienced AI Engineer to manage the design, deployment, and optimization of production-grade language model systems. This role involves building applications using both commercial LLM APIs and self-hosted open-source models, implementing RAG pipelines, and creating end-to-end LLM workflows. The ideal candidate combines practical experience integrating LLM APIs with technical expertise in deploying and optimizing local models. Key Responsibilities Design and implement high-throughput, low-latency serving architectures for LLM applications Build and maintain RAG pipelines and end-to-end LLM workflows Integrate and optimize commercial LLM APIs (OpenAI, Anthropic, Google, etc.) into production systems Develop prompt engineering techniques and prompt management systems Deploy and serve local open-source language models for specific use cases Optimize local model inference performance through efficient serving frameworks Fine-tune models to improve performance on domain-specific tasks ( Monitor and troubleshoot production LLM systems to ensure reliability Research and experiment with emerging models and techniques to improve system capabilities Document architectures, best practices, and technical decisions Collaborate with engineering teams to integrate LLM capabilities into products Communicate technical terms and recommendations to stakeholders Build evaluation frameworks to measure model quality, latency, cost, and user satisfaction Design intelligent routing and fallback strategies across multiple LLM providers Scale LLM services to handle production workloads efficiently Implement caching, batching, and request optimization strategies for both APIs and local models Required Qualifications Experience: Solid 2+ years focused on LLM applications or chatbot development Proven track record of building production LLM applications Experience integrating and optimizing commercial LLM APIs Hands-on experience deploying local models in production environments Technical Skills: Strong Python programming with emphasis on async/await patterns and production-quality code Deep understanding of transformer architectures and LLM fundamentals Experience with LLM APIs (OpenAI, Anthropic Claude, Google Gemini, or similar) Familiarity with local open-source models (Qwen, Llama, Mistral, or similar) Experience with RAG implementation using LlamaIndex, LangChain or similar frameworks Proficiency with FastAPI for building high-performance APIs Experience with vector databases (Pinecone, Weaviate, Chroma, Milvus, or similar) Working knowledge of MongoDB or other NoSQL databases Experience with Docker containerization and deployment Good to have hands-on fine-tuning experience (LoRA, QLoRA, full fine-tuning) Familiarity with local model serving frameworks (vLLM, TGI, or similar) Familiarity with LLM workflow tracing and observability frameworks (MLflow, Phoenix, Langfuse, or similar) Familiarity with Hugging Face ecosystem and transformer libraries Experience with cloud platforms (AWS, GCP, or Azure) Proficiency with Git/GitHub and version control workflows Domain Knowledge: Understanding of prompt engineering and optimization techniques Knowledge of LLM evaluation metrics and benchmarking methodologies Experience with cost optimization for LLM applications Familiarity with distributed computing and scaling strategies Understanding of LLM inference optimization (quantization, batching, caching) Preferred Qualifications Understanding of digital human technologies and multimodal applications Knowledge of MLOps practices and CI/CD for ML systems Experience with Kubernetes for container orchestration Experience with streaming inference and real-time applications Background in function calling and tool use with LLMs Familiarity with RLHF (Reinforcement Learning from Human Feedback) Experience with model distillation and knowledge compression Understanding of distributed training and GPU optimization Experience with multi-agent systems and LLM orchestration Soft Skills & Communication Excellent English communication skills (written and verbal) Excellent Chinese reading skill Ability to explain complex technical concepts to both technical and non-technical audiences Strong problem-solving and analytical thinking capabilities Self-motivated with ability to work independently and drive projects to completion Collaborative team player who thrives in fast-paced environments Passion for staying current with rapidly evolving LLM technologies Ability to balance research experimentation with production reliability requirements Excited to join us? Press APPLY or email your resume to jobs@pantheonlab.ai with: - The role you're applying for - Your availability - Expected salary We can’t wait to hear from you! NOTE: All personal data is collected for recruitment purposes only.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.