About The Company :
ARA's client is a leading IT solutions provider, offering Applications, Business Process Outsourcing (BPO) and Infrastructure services globally through a combination of technology know how, domain, and process expertise. The accolades have been garnering can be attributed to their undeterred focus in delivering quality solutions across verticals that meet the challenging requirements of their esteemed customers.
The Role : AI / ML Lead Engineer
Key Responsibilities :
- Design and implement automated systems for deploying, monitoring, and retraining machine learning models across development, testing, and production environments.
- Develop scalable ML tools and services to support model training, inference, and lifecycle management tailored to client-specific needs.
- Establish and optimize MLOps pipelines using orchestration tools (e.g., MLflow, Kubeflow) and CI / CD practices to enable continuous integration and delivery of ML models.
- Implement systems for real-time monitoring, alerting, and logging of data drift, model drift, and performance degradation.
- Work closely with data scientists, data engineers, and DevOps teams to ensure robust integration and deployment of ML solutions.
- Stay updated with emerging technologies and tools in the AI / ML and MLOps space to enhance solution reliability and scalability.
- Leverage cloud services (AWS, GCP, Azure) for secure and scalable model deployment using containerized environments (e.g., Docker, Kubernetes).
- Diagnose and resolve issues across the ML lifecycle, from data ingestion and feature engineering to model deployment and serving.
- Document technical processes, contribute to internal knowledge bases, and support MLOps maturity efforts across the organization.
- Participate in internal research initiatives and contribute to the design of reusable frameworks, tools, and accelerators for MLOps.
Skills Required :
Bachelors or masters degree in computer science, Data Science, or a related field.Strong programming skills in Python with hands-on experience in ML frameworks such as TensorFlow, PyTorch, and scikit-learn.Experience with ML workflow orchestration tools like MLflow or Kubeflow.Solid understanding of data architecture, data engineering, and data management practices.Familiarity with tools and processes used by data scientists, with experience in software development and test automation.Proven ability to design and implement MLOps pipelines in cloud environments such as AWS, Azure, or GCP.Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).Strong analytical and problem-solving abilities to address complex system-level challenges.Excellent communication and interpersonal skills, with the ability to convey technical concepts to diverse stakeholders.Knowledge of additional languages like Java, C++, or Javascript is a plus.Qualifications & Experience :
Any Graduateref : hirist.tech)