Senior MLOps Engineer

SoftServe

Abu Dhabi, UAE

Full Time

Senior

2 weeks ago

PythonMachine LearningMLOpsKubeflowMLflowKubernetes

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Key skills for this role

PythonMachine LearningMLOps

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About the Role

SoftServe is looking for a Senior MLOps Engineer to join the AI and Data Science Center of Excellence. You will guide engineering and data science teams through the ML systems production lifecycle, implement end-to-end production pipelines, and operationalize AI solutions.

Key Skills for This Role

PythonMachine LearningMLOpsKubeflowMLflowKubernetes

Responsibilities

Communicate use cases, requirements, and expectations with stakeholders
Guide engineering and data science teams through the ML systems production lifecycle
Collaborate with Data Science teams on model operationalization strategies
Work closely with product teams to deliver and operate ML systems
Implement end to end production pipelines for ML solutions
Support and continuously enhance ML software infrastructure, including CI/CD, data stores, cloud services, network configuration, security, and system monitoring
Set up scalable monitoring systems for data pipelines and ML models
Operationalize AI solutions by applying best practices in machine learning, MLOps, DevOps, and software engineering
Maintain synergy among data scientists, DevOps, and ML Engineers to build infrastructure, set up processes, and productize machine learning pipelines
Work as a consultant on different projects with a flexible schedule

Requirements

Master's degree in computer science or a related field
6+ years of relevant experience
Hands on with Python and traditional Python data science and machine learning stack
Expert in ML solutions, design patterns, and operationalization
Skilled with AI/ML and data engineering tools in any major cloud platform
Familiar with setting up CI/CD/CT pipelines for ML
Well versed in Kubeflow, MLflow, or similar
Competent with containers and container orchestration platforms (Kubernetes)
Proficient in Azure cloud services (AI studio, AI search, Azure Data Factory, Azure Functions, Cosmos DB, etc.)
Experienced with Databricks platform
Strong in requirements gathering and estimation
Experienced with designing and building feature stores

Full Job Posting

WE ARE

SoftServe is a global digital solutions company, headquartered in Austin, Texas, and founded in 1993.
With 2,000+ active projects across the USA, Europe, APAC, and LATAM, we deliver meaningful outcomes through bold thinking and deep expertise.
The AI and Data Science Center of Excellence (CoE) is SoftServe’s premier AI/ML hub, primarily based in Europe.
With 160+ experts, including data scientists, research analysts, MLOps engineers, ML and LLM architects we cover the full AI lifecycle, from problem framing to deployment.
In 2024, we delivered 150+ AI projects, including over 100 focused on Generative AI, combining scale with measurable impact.
We are a 2024 NVIDIA Service Delivery Partner and maintain strong collaborations with Google Cloud, Amazon, and Microsoft.
We also lead Gen AI Lab our internal innovation engine focused on applied research and cross functional collaboration in Generative AI.
In 2025, a key area of innovation is Agentic AI, where we design and deploy autonomous, collaborative agent systems capable of addressing complex, real world challenges at scale for our clients and internally.

IF YOU ARE

A holder of a master's degree in computer science or a related field
Having 6+ years of relevant experience
Hands on with Python and traditional Python data science and machine learning (DS/ML) stack
An expert in ML solutions, design patterns, and operationalization
Skilled with AI/ML and data engineering tools in any major cloud platform
Familiar with setting up continuous integration, continuous delivery, and continuous testing (CI/CD/CT) pipelines for ML
Well versed in Kubeflow, MLflow, or similar
Competent with containers and container orchestration platforms (Kubernetes)
Proficient in Azure cloud services (AI studio, AI search, Azure Data Factory, Azure Functions, Cosmos DB, etc.)
Experienced with Databricks platform
Experience working with healthcare data it would be a plus
Strong in requirements gathering and estimation

AND YOU WANT TO

Communicate use cases, requirements, and expectations with stakeholders
Guide engineering and data science teams through the ML systems production lifecycle
Collaborate with Data Science teams on model operationalization strategies
Work closely with product teams to deliver and operate ML systems
Implement end to end production pipelines for ML solutions
Support and continuously enhance ML software infrastructure, including CI/CD, data stores, cloud services, network configuration, security, and system monitoring
Set up scalable monitoring systems for data pipelines and ML models
Operationalize AI solutions by applying best practices in machine learning, MLOps, DevOps, and software engineering
Maintain synergy among data scientists, DevOps, and ML Engineers to build infrastructure, set up processes, and productize machine learning pipelines
Work as a consultant on different projects with a flexible schedule

TOGETHER WE WILL

Be part of a team that's shaping the future of AI and data science through innovation and shared growth.
Advance the frontier of Agentic AI by shaping intelligent multi agent ecosystems that drive autonomy, scalability, and measurable business value.
Have access to world class training, cutting edge research, and collaborate with top industry partners.
Maintain a synergy of Data Scientists, DevOps team, and ML Engineers to build infrastructure, set up processes, productize machine learning pipelines, and integrate them into existing business environments
Communicate with the world leading companies from our logos portfolio
Enjoy the opportunity to work with the latest modern tools and technologies on various projects
Participate in international events and get certifications in cutting edge technologies
Have access to powerful educational and mentorship programs
Revolutionize the software industry and drive innovation in adaptive self learning technologies by leveraging multidisciplinary expertise.

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