Who You’ll Work With

Working in Engineering Productivity (EngProd), you will collaborate and work with other engineers to design, build, scale, and operate the systems that the rest of Arista’s development teams use. The EngProd team uses industry-standard systems like Ansible, Jenkins, Kubernetes, Grafana, Spinnaker, MySQL, ElasticSearch, Google Cloud, and Varnish and also internal systems that we’ve built from the ground-up to automate CI/CD, testing, analysis, and visualization. We believe observability is one of the cornerstones of a stable system, and endeavor to make sure we can monitor and visualize all aspects of our tooling and infrastructure.

Arista Software Engineering roles demand a strong and broad software engineering background. While the primary focus area for this role is EngProd, the role will not be limited to any single aspect of software development at Arista. It will cover all aspects of software development spanning the addition of new features, debugging problems and fine tuning code for scalability and performance. You will own your projects from definition to deployment and customer interactions, and you will be responsible for the quality of everything you deliver. 

Arista Software Engineering prides itself on a supportive and growth oriented culture. Over time, as your interests change, coupled with evolving business needs you will have the opportunity to switch from EngProd to other areas, if you like.

What You'll Do

• Building, integrating and maintaining tools and infrastructure facilitating internal development and testing.
• Improve maintainability of build system
• Evaluate new tools
• Improve speed of information back to the development team within the build systems and processes
• Troubleshoot and resolve systems and network issues.
• Adherence to infrastructure-as-code principles.
• Proactively ensure the highest levels of systems and infrastructure availability.
• Participate in the design and implementation of new systems and infrastructure projects.

Key Responsibilities:

• Design and implement the enterprise-wide data architecture strategy, including hands-on development of proof-of-concepts for new technologies and frameworks
• Architect and actively develop complex distributed systems that can scale across multiple regions and handle diverse data workloads
•  Develop, manage, and optimize ETL processes using Azure Data Factory to ensure accurate data integration across global databases
• Design, build, and maintain Microsoft SQL Server databases, ensuring data is well-organized, accessible, and secure
• Lead by example through hands-on development and implementation of advanced data processing frameworks that can be reused across multiple teams
• Define technical standards and best practices for data engineering across the organization through practical implementation and documentation
• Evaluate emerging technologies through proof-of-concept development and make recommendations for adoption or retirement of data technologies
• Identify and implement performance optimization strategies for ETL processes and reports to ensure optimal user experience
• Monitor and troubleshoot complex data processes to ensure uninterrupted service to global teams, including working occasionally outside regular business hours to support multiple time zones
• Create and maintain comprehensive technical documentation, architectural diagrams, and best practices
• Ensure data governance and compliance principles are maintained across all architectural decisions
• Communicate technical information to non-technical stakeholders effectively, tailoring the messaging as needed for diverse audiences
• Mentor and guide team members in technical best practices and architectural principles
• Collaborate effectively with cross-functional teams, including data scientists, analysts, and business stakeholders

Required Technical Skills:

• Expert-level proficiency in SQL and database optimization techniques
• Hands-on expertise in Azure services, particularly Azure Data Factory, Azure SQL Database, and Azure Analysis Services
• Deep practical experience with Data Warehousing using Kimball and STAR schema
• Strong coding abilities in at least one programming language (Python, Java, or C#)
• Hands-on experience with distributed computing frameworks
• Practical experience implementing data streaming solutions
• Expert knowledge of performance tuning and optimization techniques

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Are you curious, motivated, and forward-thinking? At FIS you’ll have the opportunity to work on some of the most challenging and relevant issues in financial services and technology. FIS is a leading Fintech provider with wide range of Products and Services. Our talented people empower us, and we believe in being part of a team that is open, collaborative, entrepreneurial, passionate and above all fun.

About the role

FIS is seeking an experienced and dynamic MLOps engineer, who will be a subject matter expert in the development of a Cloud Based solution/product. You will have at least 3 years of experience in Machine Learning & ML OPS and proven experience in deploying model on Azure Cloud. Masters is Data Science or Machine learning & experience in Fintech Capital market space is an added advantage.

What you will be doing:

• Participating in Daily Scrum for developing Machine learning solution. Working in Agile development framework

• Understanding the business problems and working with various business & Technical stakeholders.

• Doing exploratory Data analysis and understanding meta data.

• Creating and reviewing ML Models.

• Understanding on Supervised Learning, Unsupervised Learning and Reinforcement learning

• Understanding on Generative AI and prompt engineering

• Model fine tuning.

• Partnering with Data Engineers, ML engineers, developers, and business teams to test your models in production.

• Building and maintaining operational tools for deployment, monitoring, and analysis of Azure infrastructure and systems.

• Administering and troubleshooting Linux based, Dockerized, Kubernetes-orchestrated systems.

• Maintaining/Supporting the solution post development.

What You Will Need:

• Experience with Time Series forecasting Models: ARMA, ARIMA, SARIMA, Vector Auto regression, Smoothing, Kalman Filtering,

• Anomaly Detection: Isolation forests, SVM, Decision Trees and Ensemble Learning

• Deep Learning Basics: RNN, LSTMs, Transformer Architecture, BERT, GPT, Roberta

• Generative AI Prompt engineering: Defining & working on prompts, Fine Tuning LLMs, RAG

• Lang Chain: Chain, Prompts, Agents, Vector stores, Document loaders

• Deploying ML Model on Cloud: Model end point deployment, Docker, Understanding REST APIs, Bit bucket

• Cloud Azure Services: Chat GPT 3.5 Turbo, Azure AI Search, Azure Content Safety, Cosmos DB, Blob storage, Data Bricks, Azure Kubernetes services, Experience in creating ML pipelines on Azure.

• Good Communication Skills: Listening, Comprehending, Speaking.

• Understanding of Application Architecture/Microservices: Application Architecture, Microservice based Architecture.

• Programming: Python, SQL; familiar with Python Libraries like SCIKIT Learn, Tensorflow, Keras, pytorch, scikit-learn, seaborn , matplotlib

Added bonus if you have:

• Experience Working in the financial industry.

What we offer you

• An opportunity to make your mark in a world leading global Fintech

• A flexible hybrid environment with a broad spectrum of opportunities

• A competitive salary package with medical and personal insurances and employee shareholding

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Privacy Statement

FIS is committed to protecting the privacy and security of all personal information that we process in order to provide services to our clients. For specific information on how FIS protects personal information online, please see the Online Privacy Notice.

Sourcing Model

Recruitment at FIS works primarily on a direct sourcing model; a relatively small portion of our hiring is through recruitment agencies. FIS does not accept resumes from recruitment agencies which are not on the preferred supplier list and is not responsible for any related fees for resumes submitted to job postings, our employees, or any other part of our company.

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