Machine Learning Engineer

About Synechron:

Synechron Singapore is a leading consulting and technology services firm specializing in the financial services sector. Our Singapore office focuses on delivering innovative digital transformation solutions to top-tier banks, insurers, and capital markets. We cultivate a diverse and inclusive environment where talent thrives, offering opportunities for professional growth and development. With access to cutting-edge technologies, our teams work collaboratively to create impactful solutions that enhance operational efficiency and customer experiences. Synechron Singapore is committed to excellence and actively engages in community initiatives, making a positive impact in the region.

Our 14,500+ associates work across the globe at more with 100+ clients and 58 offices.

We are seeking a skilled and motivated Data Engineer with expertise in Hadoop, Spark, OpenShift Container Platform (OCP), and DevOps practices. As a Data Engineer, you will be responsible for designing, developing, and maintaining efficient data pipelines, processing large-scale datasets. Your expertise in Hadoop, Spark, OCP, and DevOps will be crucial in ensuring the availability, scalability, and reliability of our ML Solutions.

Responsibilities:

As an ML Engineer, your pivotal role involves operationalizing ML Models developed by data scientists. You will serve as the focal point for ML model refactoring, optimization, containerization, deployment, and quality monitoring. Your main responsibilities will include:

Conduct reviews for compliance of the ML models in accordance with overall platform governance principles such as versioning, data / model lineage, code best practices and provide feedback to data scientists for potential improvements

Develop pipelines for continuous operation, feedback and monitoring of ML models leveraging best practices from the CI/CD vertical within the MLOps domain. This can include monitoring for data drift, triggering model retraining and setting up rollbacks.

Optimize AI development environments (development, testing, production) for usability, reliability and performance.

Have a strong relationship with the infrastructure and application development team in order to understand the best method of integrating the ML model into enterprise applications (e.g., transforming resulting models into APIs).

Work with data engineers to ensure data storage (data warehouses or data lakes) and data pipelines feeding these repositories and the ML feature or data stores are working as intended.

Evaluate open-source and AI/ML platforms and tools for feasibility of usage and integration from an infrastructure perspective. This also involves staying updated about the newest developments, patches and upgrades to the ML platforms in use by the data science teams.

Technical Skills :

Proficiency in Python used both for ML and automation tasks

Good knowledge of Bash and Unix/Linux command-line toolkit is a must-have.

Hands on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions or similar tools is a must-have.

Knowledge of OpenShift / Kubernetes is a must-have.

Good understanding of ML libraries such as Panda, NumPy, H2O, or TensorFlow.

Knowledge in the operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g., Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, Dataiku, H2O, or DKube).

Knowledge of Distributed Data Processing framework, such as Spark, or Dask.

Knowledge of Workflow Orchestrator, such as Airflow or Ctrl-M.

Knowledge of Logging and Monitoring tools, such as Splunk and Geneos.

Experience in defining the processes, standards, frameworks, prototypes and toolsets in support of AI and ML development, monitoring, testing and operationalization.

Experience in ML operationalization and orchestration (MLOps) tools, techniques and platforms. This includes scaling delivery of models, managing and governing ML Models, and managing and scaling AI platforms.

Knowledge of cloud platforms (e.g. AWS, GCP) would be an advantage.

Soft Skills:

Good knowledge of Devops process and principles

Strong in Software Engineering fundamentals

Excellent communication skills

Attention to detail

Analytical mind and problem-solving aptitude

Strong Organizational skills

Visual Thinking

Position will be Onsite at customer location/or Synechron Office.

At Synechron we believe in designing a better, more sustainable workforce. We recognize the benefits of flexible, remote working arrangements for eligible roles and are committed to providing enriching careers, no matter the work arrangement. This position is eligible for a remote/onsite/hybrid work arrangement. Additional information about this work arrangement will be provided by your interview team. Explore the flexibility and challenge that working for Synechron can provide.

At Synechron, we recognize that diversity is essential to our success. We are committed to fostering an inclusive environment where all employees feel valued and empowered to share their unique perspectives. We embrace differences in race, ethnicity, gender, sexual orientation, age, religion, disability, and cultural background, understanding that these attributes enrich our workplace and enhance our service to clients.

Our initiatives aim to promote equal access to opportunities and ensure that diverse voices are heard. By championing diversity and inclusion, we strengthen our ability to innovate and meet the challenges of the financial services industry.