AI/Machine Learning Engineer

18 hours ago


Coimbatore, Tamil Nadu, India beBeeArtificial Full time ₹ 2,50,00,000 - ₹ 3,00,00,000
Senior AI/ML Engineer: Designing and Deploying End-to-End Solutions

About the Role:

We are seeking a highly skilled Senior AI/ML Engineer to lead our data science initiatives. As a key member of our team, you will be responsible for designing, developing, and deploying end-to-end AI/ML solutions that drive business value.

Key Responsibilities:

  • Design and develop innovative AI/ML solutions using Python and ML frameworks such as PyTorch, TensorFlow, Scikit-learn, and Hugging Face.
  • Lead projects involving large language models (LLMs), natural language processing (NLP), recommendation engines, and predictive modeling.
  • Build, optimize, and maintain data pipelines and APIs for ML services, ensuring seamless integration with cloud platforms.
  • Deploy and scale ML models on cloud platforms with containerization and CI/CD, utilizing tools like Docker and Kubernetes.
  • Collaborate with cross-functional teams to deliver production-ready AI systems that meet business requirements.
  • Ensure model performance, scalability, monitoring, and explainability through rigorous testing and evaluation.

Requirements:

  • Bachelor's or Master's degree in Computer Science, Data Science, or related field.
  • 5–6 years of hands-on experience in AI/ML and data science, with a strong focus on software development and engineering principles.
  • Expertise in Python and ML frameworks, including practical experience with LLMs, embeddings, vector databases, and RAG pipelines.
  • Proven background in recommendation systems, personalization, and predictive modeling, with a strong understanding of statistical analysis and machine learning concepts.
  • Proficiency in SQL/NoSQL databases and data pipeline development, with knowledge of data warehousing and business intelligence tools.
  • Familiarity with APIs & microservices, including FastAPI, Django REST, and Flask.
  • Experience with cloud ML deployments on Azure, AWS, and GCP, with a strong understanding of cloud computing architectures and security best practices.
  • Good knowledge of Docker, Kubernetes, Git, and CI/CD for production ML, with a focus on automation and DevOps principles.


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