AI/ML Engineer

We are seeking a skilled and innovative AI/ML Engineer with 4 10 years of experience to join our dynamic data science and engineering team. The ideal candidate will have strong expertise in building and deploying machine learning models and AI solutions in production environments. You will work closely with data scientists, software engineers, and business stakeholders to solve complex problems using cutting-edge AI/ML technologies.

Key Responsibilities

• Design, develop, train, and deploy machine learning models for real-world business problems.
• Perform data preprocessing, feature engineering, and exploratory data analysis (EDA).
• Work on NLP, Computer Vision, or Predictive Modeling projects based on business needs.
• Collaborate with data engineers to build scalable data pipelines and ensure high data quality.
• Evaluate and improve model performance through experimentation, tuning, and A/B testing.
• Implement and maintain MLOps workflows (model versioning, CI/CD, monitoring).
• Stay up to date with the latest trends in AI/ML research and tools.

Required Skills Experience

• 4 7 years of hands-on experience in AI/ML model development and deployment.
• Proficient in Python and libraries such as scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, pandas, NumPy.
• Experience with NLP, Computer Vision, or Time Series modeling.
• Familiarity with cloud platforms like AWS, Azure, or GCP for model deployment.
• Understanding of MLOps tools such as MLflow, DVC, Airflow, Docker, and Kubernetes.
• Experience with data querying using SQL and data handling using APIs or data lakes.
• Exposure to LLMs or Generative AI frameworks (OpenAI, LangChain, Hugging Face).

Tech Stack

Python scikit-learn TensorFlow PyTorch Keras XGBoost NLP Computer Vision AWS / Azure / GCP MLflow Docker Kubernetes LangChain Hugging Face

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.