ML OPS - gurugram, India - Talent Socio

Talent Socio gurugram, India

2 weeks ago

Description

Overview:

Our client provides digital strategy, innovative technology and supply chain solutions to large public and private organizations around the globe. With thousands of IT engineers, hundreds of application developers and unmatched labs for testing and deploying technology at scale, our client helps customers bridge the gap between IT and the business. By bringing leading technology companies together in a physical yet virtualized environment through its Advanced Technology Center, it integrates individually impressive technologies to produce game-changing solutions.

Roles and Responsibilities:

Design and implement scalable data pipelines and engineering infrastructure to support machine learning systems at scale.

Collaborate with data scientists to operationalize and deploy offline models into production-ready systems.

Develop and deploy robust tools and services to streamline machine learning training, inference, and monitoring processes.

Evaluate and integrate new technologies to enhance the performance, maintainability, and reliability of machine learning systems.

Apply best practices of software engineering, including CI/CD, automation, and version control, to machine learning workflows.

Ensure audibility, versioning, and data security standards are met throughout the model development lifecycle.

Lead the development and deployment of proof-of-concept machine learning systems to demonstrate feasibility and efficacy.

Automate the deployment and scaling of machine learning models in production environments.

Monitor and maintain the performance, accuracy, and reliability of deployed machine learning models.

Establish and manage continuous integration and delivery pipelines for efficient model deployment.

Collaborate closely with data scientists, software engineers, and other stakeholders to ensure effective deployment and operation of machine learning models.

Enforce security, privacy, and compliance measures to safeguard machine learning models and associated data.

Stay abreast of the latest advancements and trends in MLOps to drive innovation and efficiency.

Requirements:

Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.

3-7 years of experience in data engineering, software development, or a related field.

Proven experience in building end-to-end systems as a Platform Engineer, MLOps Engineer, or similar role.

Hands-on experience with MLOps tools such as MLFlow, Azure Machine Learning, or equivalent.

Proficiency in Python and strong software engineering skills in developing complex, multi-language systems.

Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and database systems.

Demonstrated ability to build custom integrations between cloud-based systems using APIs.

Familiarity with containerization technologies (e.g., Docker) and Kubernetes in cloud environments.

Ability to translate business requirements into technical specifications effectively.

Solid understanding of software testing, benchmarking, and continuous integration practices.

Exposure to machine learning methodologies and best practices.

2 years of experience working with Cloud Platforms (e.g., Microsoft Azure, AWS, GCP).

2 years of experience in Python, Databricks/Hadoop.

2 years of experience in MLOps.

Good to have:

Curiosity, consultative mindset, and eagerness to explore new technologies.

Ownership mindset with a commitment to delivering high-quality work.

Strong communication skills to engage with external stakeholders across global clients and the internal team of engineering, product, and business.

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