Job Description - ML Engineer

About the Job

About Aligned Automation

At Aligned Automation, we live by our "Better Together" philosophy to build a better world. As a strategic service provider to Fortune 500 companies, we help digitize enterprise operations and drive impactful business strategies. Our purpose goes beyond projects—we strive to deliver meaningful, sustainable change that shapes a more optimistic and equitable future.

Our culture is deeply rooted in our 4Cs—Care, Courage, Curiosity, and Collaboration—ensuring that each employee is empowered to grow, innovate, and thrive in an inclusive workplace.

Job Description: 

We are looking for a
seasoned ML Engineer (MLOps) to join our team and drive the
development and deployment of scalable machine learning solutions. The ideal
candidate will have deep expertise in building robust ML pipelines, integrating
large language models (LLMs), and managing model lifecycle using tools like
MLFlow. You will work in agile teams, contributing to high-quality code and
ensuring smooth operations across the ML infrastructure.

Key Responsibilities:

  • Participate in Scrum
    ceremonies and contribute to sprint planning and retrospectives.

  • Scope and resolve technical issues
    related to ML pipelines and infrastructure.

  • Write and implement clean,
    scalable, and maintainable code for ML workflows.

  • Submit and manage pull requests,
    ensuring code quality through liners and scanners.

  • Conduct and participate in code
    reviews to maintain high standards.

  • Collaborate with data scientists and
    engineers to deploy and monitor ML models.

  • Manage model lifecycle
    using MLFlow Hub and integrate with cloud-native solutions.

Work
with LLMs to build intelligent applications and services.


Technical Skills:

  • Strong proficiency
    in Python for ML and MLOps tasks.

  • Experience
    with databases (especially Postgres).

  • Familiarity with object storage
    systems like Amazon S3.

  • Hands-on experience
    with LLMs and their integration into production systems.

  • Proficient in using MLFlow
    Hub for model tracking and deployment.

  • Comfortable
    with GitHub workflows and version control.

Technology Stack:

  • Programming &
    Scripting: Python

  • Databases: Postgres
  • Cloud & Storage: Amazon ECS,
    S3

  • ML Tools: MLFlow Hub, LLMs
  • Version Control & Collaboration:
    GitHub




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