Job
Description
Job Title: AI/ML Engineer Experience: 6+ Years Location: Remote Employment Type: Full-Time Job Summary: We are looking for a passionate and experienced AI/ML Engineer with 6+ years of hands-on experience in developing, deploying, and optimizing machine learning and deep learning models. The ideal candidate should have strong software engineering fundamentals, experience with end-to-end ML pipelines, and a deep understanding of AI/ML concepts. This is a remote position with the opportunity to work in a fast-paced, collaborative, and innovative environment. Key Responsibilities: Design, develop, and deploy machine learning models for real-world business use cases. Build end-to-end ML pipelines including data preprocessing, model training, evaluation, and deployment. Work with large-scale datasets using distributed computing tools like Spark or Dask. Deploy models in production using tools like Docker, Kubernetes, and cloud services (AWS/GCP/Azure). Collaborate with data scientists, data engineers, product managers, and software engineers. Optimize model performance, reduce latency, and ensure robustness in real-time environments. Research and experiment with new algorithms and ML approaches to improve existing solutions. Mandatory Skills: Programming Languages: Proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch), and working knowledge of SQL . ML/AI Skills: Strong understanding of supervised/unsupervised learning, deep learning (CNNs, RNNs, Transformers), NLP, and classical ML algorithms. Model Deployment: Experience with model serving frameworks (FastAPI, Flask, TensorFlow Serving, TorchServe) and MLOps practices. Cloud Platforms: Hands-on experience with at least one major cloud provider ( AWS, Azure, or GCP ) for AI/ML workloads. Tools & Frameworks: Familiarity with MLFlow, Airflow, DVC, Docker, Kubernetes. Data Handling: Experience with handling large datasets, data cleaning, feature engineering, and working with structured/unstructured data. Preferred Qualifications: Experience with Vector Databases , LLMs , LangChain , or Generative AI solutions. Knowledge of CI/CD in ML workflows and Infrastructure as Code (e.g., Terraform). Background in statistics, optimization , and experiment design . Exposure to Big Data tools like Spark, Hive, or Hadoop is a plus. Published papers, GitHub contributions, or Kaggle competition experience is an advantage.
