Senior Machine Learning Engineer - Python (4-6 yrs)

Skills : Apache Kafka, CI/CD, RESTful APIs, Python (Programming Language, Databricks SQL, PySpark, Talend, Redshift & SQL.

Position Summary :

Senior Machine Learning Infrastructure Engineer to lead the design, implementation, and optimization of our end-to-end ML infrastructure. In this role, you will provide technical leadership, guide a team of ML engineers, and drive best practices for model development, deployment, and scaling.

You will work closely with data scientists, software engineers, and stakeholders to build high-quality, scalable, and reliable ML systems. This includes designing automated CI/CD pipelines, optimizing model performance, and leveraging cloud and containerization technologies to ensure seamless deployment.

Responsibilities :

- Lead the implementation of a cutting-edge ML infrastructure, encompassing all facets from model inception to deployment, ensuring adherence to best practices and high overall quality standards.

- Provide exceptional technical leadership, mentoring, and guidance to a team of machine learning engineers, fostering a culture of continuous learning and innovation.

- Take ownership of critical projects and initiatives, providing project leadership, and ensuring successful delivery through effective project management and communication.

- Engage with stakeholders across the group, understanding their needs and working through the complexity and conflicting goals.

- Collaborate closely with data scientists to translate intricate model requirements into optimized data pipelines, ensuring impeccable data quality, processing, and integration.

- Spearhead the establishment of best practices for model versioning, experiment tracking, and model evaluation to ensure transparency and reproducibility.

- Architect and execute model deployment strategies, harnessing containerization (Docker) and orchestration (Kubernetes) for exceptional scalability and reliability.

- Engineer automated CI/CD pipelines that facilitate seamless model deployment, monitoring, and continuous optimization.

- Define and refine performance benchmarks, and optimize models and infrastructure to achieve peak efficiency, scalability, and robustness.

- Remain at the forefront of industry trends and emerging technologies, expertly integrating the latest advancements into our ML ecosystem.

Qualifications :

- Extensive experience orchestrating the development of end-to-end machine learning infrastructure for intricate and large-scale applications.

- Proven record of transformative leadership, guiding technical teams to achieve remarkable outcomes and innovation.

- Proven track record of delivering sophisticated ML solutions with high quality.

- Exceptional problem-solving and analytical skills, with a passion for tackling complex technical and business problems. challenges.

- Solid understanding of data structures, algorithms, and software design principles.

- Profound mastery of machine learning frameworks such as TensorFlow, PyTorch, or equivalent, coupled with Python programming.

- Deep expertise in containerization (Docker) and orchestration (Kubernetes) for orchestrating complex machine learning applications.

- Thorough comprehension of software engineering principles, version control (Git), and collaborative development workflows.

- Adeptness with cloud platforms (AWS or Azure) and utilization of cloud-native services for crafting robust ML infrastructure.

- Track record of successfully integrating DevOps practices, continuous integration, and continuous deployment (CI/CD) pipelines.

- Excellent communication and interpersonal skills, with the ability to collaborate effectively in a team-oriented environment.