Senior AI Engineer
我们希望聘请热情的高级人工智能工程师，帮助将数据转化为智能，生产准备好的解决方案。你将在整个人工智能堆中工作：传统机器学习模型，大型语言模型 (LLM),计算机视觉管道和分析/预测工作流程。如果你喜欢探索数据，构建最先进的模型和运送可靠的 AI 服务，我们很想见到你。责任 · 模型开发 设计，训练，细节调整和评估包括经典 ML,深度学习 （CNN,变压器）和生成型 AI （LLMs,传播）. ·数据探索和分析 进行探讨数据分析，统计测试以及时间系列/预测以信息特征，提示和业务 KPI. ·端到端管道 构建可复制的工作流程用于数据摄入，功能工程/提示存储，培训，CI/CD 和自动监控。·LLM&Agentic AI Engineering 工艺提示，取回增强生成 (RAG) 管道和自治/辅助代理；精细调 LMS 在特定领域的数据集上提高准确性并与事件需求一致输出。·自动化和整合 将产品组件作为微服务和人工智能驱动的工作流程；集成与调整工具 （空流，先）和商业 API 来自动化决策管道。·持续学习 追踪 LLM,视觉和分析方面的进步；与更广泛的工程团队分享见解和最佳实践。·指导年轻工程师并为技术方向和工程最佳实践做出贡献。要求 ·计算机科学，数学或相关领域的学士学位。· 5年以上在 AI/ML 项目上工作的专业经验。·善于掌握英语 （书面和口头）. ·熟练使用 Python 和核心图库 (PyTorch / TensorFlow, sc-learn, pandas, NumikitPy). · 熟悉至少一个：OpenCV,脸变换器，LangChain,MLflow 或类似的方法。· 善于掌握软件工程最佳实践：Git,代码审查，测试，CI.优先资格。• 对性能关键模块的 C++或 C#知识。·经验通过 Docker,Kubernetes 或云 AI 服务部署模型。·接触向量数据库和 RAG 工作流程。· 技能在使用 C++或 C#.
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We are looking to hire passionate Senior AI Engineers to help turn data into intelligent, production-ready solutions. You will work across the full AI stack: traditional machine-learning models, large language models (LLMs), computer-vision pipelines, and analytics / forecasting workflows. If you enjoy exploring data, building state-of-the-art models, and shipping reliable AI services, we would love to meet you.
Responsibilities
- Model Development – Design, train, fine-tune, and evaluate models spanning classical ML, deep learning (CNNs, transformers), and generative AI (LLMs, diffusion).
- Data Exploration & Analytics – Conduct exploratory data analysis, statistical testing, and time-series / forecasting to inform features, prompts, and business KPIs.
- End-to-End Pipelines – Build reproducible workflows for data ingestion, feature engineering / prompt stores, training, CI/CD, and automated monitoring.
- LLM & Agentic AI Engineering – Craft prompts, retrieval-augmented generation (RAG) pipelines, and autonomous/assistive agents; fine-tune LLMs on domain-specific datasets to boost accuracy and align outputs with product requirements.
- AI Automation & Integration – Expose AI components as micro-services and event-driven workflows; integrate with orchestration tools (Airflow, Prefect) and business APIs to automate decision pipelines.
- Continuous Learning – Track advances in LLMs, vision, and analytics; share insights and best practices with the wider engineering team.
- Mentor junior engineers and contribute to technical direction and engineering best practices.
Requirements
- BSc in Computer Science, Mathematics, or related field.
- 5+ years of professional experience working on AI/ML projects.
- Good command of English (written and spoken).
- Proficient in Python and core libraries (PyTorch / TensorFlow, scikit-learn, pandas, NumPy).
- Solid understanding of machine-learning algorithms, deep-learning fundamentals, and basic statistics.
- Experience with data wrangling and visualization (Matplotlib / Plotly) and exploratory analysis.
- Familiarity with at least one of: OpenCV, Hugging Face Transformers, LangChain, MLflow, or similar.
- Good grasp of software-engineering best practices: Git, code reviews, testing, CI.
Preferred Qualifications
- Knowledge of C++ or C# for performance-critical modules.
- Experience deploying models via Docker, Kubernetes, or cloud AI services.
- Exposure to vector databases and RAG workflows.
- Skill in BI / dashboard tools (Power BI, Tableau, Streamlit) or time-series frameworks (Prophet, statsmodels).
- Familiarity with MLOps / LLMOps tooling (DVC, MLflow Tracking, Weights & Biases, BentoML).
- Experience with image processing techniques (e.g., OpenCV, image segmentation, feature extraction)
- Experience with Spark (PySpark) and distributed data processing, including usage of platforms such as Databricks, AWS EMR, or GCP Dataproc.
- Strong SQL skills and experience working with large-scale datasets, including partitioning and performance tuning.
- Familiarity with modern data lake architectures and scalable data storage concepts.
Originally posted on Himalayas