AI/ML Engineer

1 week ago

Lahore, Punjab, Pakistan HR POD Careers Full time
Requirements:
  • 2-6 years of experience in AI/ML development with a focus on large language models (LLMs).
  • Proficiency with Pydantic for data validation and LangChain frameworks for AI workflow orchestration.
  • Strong Python programming skills, with experience using ML libraries such as PyTorch or TensorFlow.
  • Familiarity with model deployment, monitoring, and cloud environments (AWS preferred, including SageMaker, EC2, S3).
  • Experience working with databases for ML workflows such as PostgreSQL, Redis, or specialized vector databases (e.g., Pinecone, Weaviate).
  • Ability to participate in architectural discussions involving AI model integration, scalable infrastructure design, and data pipeline optimization.
  • Top-notch communication skills, both written and verbal.
  • Ability to collaborate effectively across multidisciplinary teams.

Responsibilities:
  • Design, develop, and optimize AI/ML models, particularly large language models (LLMs), to address business-specific use cases.
  • Utilize LangChain to orchestrate AI workflows and integrate them into scalable production pipelines.
  • Implement Pydantic for data parsing and validation within ML applications.
  • Build and maintain robust machine learning systems using Python, with a strong grasp of PyTorch or TensorFlow frameworks.
  • Deploy and monitor models in production environments, leveraging AWS services such as SageMaker, EC2, and S3.
  • Develop and maintain scalable and efficient data pipelines, incorporating PostgreSQL, Redis, and vector databases like Pinecone or Weaviate.
  • Participate in architectural design discussions for AI model integration, data flow optimization, and infrastructure scalability.
  • Collaborate closely with cross-functional teams including data engineers, backend developers, and product managers to ensure seamless integration of AI components.
  • Contribute to code reviews, documentation, and best practices to uphold high engineering standards.
  • Communicate complex AI concepts and results effectively to both technical and non-technical stakeholders.
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