Bengaluru, India1 – 3 Years in ML Development1 month ago
RoleAI Engineer – Machine Learning
LocationBengaluru, India
Experience1 – 3 Years in ML Development
QualificationB.E / B.Tech / M.E / M.Tech in CS or related
Job Description
As an AI Engineer at ZS, you will build and refine ML Engineering platforms and components, ensuring they can scale to massive datasets while meeting strict SLAs. You will be responsible for orchestrating end-to-end model pipelines, including feature engineering, inferencing, and continuous model training. A critical part of your role involves implementing MLOps practices, such as tracking model KPIs, monitoring model drift, and establishing feedback loops using tools like MLFlow .
You will write production-ready, testable code that accounts for edge cases and errors. Collaborating with client-facing teams, you will gather technical requirements and translate them into high-performance ML models. Working in an Agile Scrum environment, you will use version control and bug tracking tools to deliver high-quality code. Additionally, you will conduct proof-of-concepts (PoCs) to research latest architecture patterns, ensuring ZS remains a leader in technology-enabled consulting.
Key Responsibilities
Build and scale ML algorithms to work on massive data sets and strict SLAs.
Orchestrate model pipelines using Airflow for feature engineering and inferencing.
Implement MLOps for model drift tracking and performance measurement via MLFlow .
Write production-ready Python or PySpark/Scala code with robust unit testing.
Follow architecture guidelines and participate in periodic design/code reviews.
Deploy and manage ML models on cloud services like AWS, Azure, or GCP .
Utilize distributed computing frameworks like Spark for high-performance tasks.
Communicate work progress and dependencies effectively during Scrum ceremonies.
Skills & Eligibility
Education: Degree from a top university in Computer Science or a related field.
ML Fundamentals: Solid understanding of ML algorithms and model scalability.
Programming: Expert proficiency in Python and PySpark/Scala .
MLOps & Platforms: Experience with MLFlow, SageMaker, or Kubeflow.
Data Engineering: High expertise in SQL and distributed computing (Spark).
Orchestration: Experience with pipeline tools such as Airflow .
Soft Skills: Strong problem-solving approach and fluency in English.
Attributes: Bold ideas, curiosity for learning, and a collaborative spirit.
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