3 - 5 years

11 - 15 Lacs

Posted:7 hours ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

We are seeking a mid-level AI Engineer to join our team focused on building scalable, production-ready AI solutions that support analytics, automation, and intelligent decision-making. This role will contribute to the development and deployment of machine learning models, generative AI systems, and AI-enhanced data workflows across platforms.

Key Responsibilities

  • Model Development & Deployment

    : Design, train, and deploy machine learning and deep learning models using frameworks like TensorFlow, PyTorch, and Scikit-learn.
  • Generative AI & LLMs

    : Implement and fine-tune large language models (LLMs) using tools such as LangChain, RAG, and vector databases. Experience with GPTs, LLaMA, or similar models is preferred
  • MLOps & GenAIOps

    : Use tools like MLflow and Docker to manage model lifecycle, reproducibility, and scalability. Support production-grade GenAI systems
  • Data Engineering Collaboration

    : Work closely with data engineers to ensure robust data pipelines and infrastructure for model training and inference.
  • Integration & APIs

    : Develop APIs and microservices to integrate AI models into enterprise applications and workflows.
  • Monitoring & Optimization

    : Continuously monitor model performance and retrain as needed to maintain accuracy and relevance.
  • Security & Governance

    : Ensure AI systems comply with enterprise security and data governance standards.

Skills & Qualifications

  • 3-5 years of experience in AI/ML engineering or related roles.
  • Proficiency in Python and experience with AI frameworks (TensorFlow, PyTorch).
  • Familiarity with cloud platforms (AWS, Azure, GCP) for model deployment.
  • Experience with MLOps tools (MLflow, Docker) and GenAI deployment.
  • Strong understanding of LLMs, NLP, and computer vision techniques.
  • Ability to write clean, efficient, and reusable code.
  • Experience with RESTful APIs and microservices architecture.

Preferred Experience

  • Exposure to educational technology or enterprise data environments.
  • Experience integrating AI into transactional systems.
  • Familiarity with data warehouses and data governance frameworks.

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