About the Job
Job Description
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 Title: ML Engineer
Experience: 7 to 9
Years
Employment Type: Full‑time
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
