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AI Engineer (Python)

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Hybrid

AI Engineer (Python)

A7 Recruitment Corporation

AI Engineer (Python)

Skills

Google Cloud PlatformSQL QueriesData EngineeringDockerReactPythonData ScienceData VisualizationMicroservicesTensorFlowRData AnalysisMachine LearningAIAWSKubernetesAzure

8 days ago

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Important information


Contract type:

Permanent

Location:

Metro Manila, Philippines

Work mode:

Hybrid

Published on:

12 October 2026

What they need


Work location: BGC

Work schedule: Mid shift / 12 PM (9:30 am to 10 am IST) start of shift

Work arrangement: 50% hybrid

Responsibilities

  • Developing and implementing advanced data solutions to transform raw data into actionable insights
  • Leveraging statistical modeling and data visualization techniques to solve complex business problems
  • Utilizing machine learning algorithms and tools such as TensorFlow and Scikit-Learn to enhance data-driven decision-making
  • Conducting exploratory and descriptive analysis to inform strategic decisions and drive business growth
  • Building and maintaining data pipelines and data lakes to support large-scale data processing
  • Collaborating with clients to understand their data needs and deliver tailored analytics solutions
  • Applying programming skills in languages such as C++ and R to develop and optimize data science workflows
  • Confirming data quality and integrity through rigorous data engineering practices
  • Mentoring junior team members in data science methodologies and techniques
  • Engaging in continuous learning to stay updated with the latest advancements in artificial intelligence and data science

What You Must Have

  • At least a Bachelor's degree

  • At least 4 to 7 years of experience

  • Oral and written proficiency in English required

  • Strong full-stack or backend development skills, including Python, Angular or React, RESTful APIs, microservices, SQL/NoSQL databases, testing, and software architecture.

  • Hands-on experience building AI-integrated applications, using LLMs, RAG, embeddings, vector databases, and frameworks such as LangChain, LlamaIndex, or Autogen.

  • Proven cloud and DevOps capability, including Azure, AWS, or GCP deployment, Docker, Kubernetes, CI/CD, Git, security, reliability, and scalability.

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