Cloud Infrastructure Engineer – AWS & ML Tooling Job at Zelis

Senior ML Platform Engineer

At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP e...

Location

United States , Boston

Salary:

150000.00 - 210000.00 USD / Year

Whoop

Expiration Date

Until further notice

Requirements

Bachelor’s or Master’s Degree in Computer Science, Engineering, or a related field
or equivalent practical experience
5+ years of experience in software engineering with a focus on ML infrastructure, cloud platforms, or MLOps
Strong programming skills in Python, with experience in building distributed systems and REST/gRPC APIs
Deep knowledge of cloud-native services and infrastructure-as-code (e.g., AWS CDK, Terraform, CloudFormation)
Hands-on experience with model deployment platforms such as AWS SageMaker, Vertex AI, or Kubernetes-based serving stacks
Proficiency in ML lifecycle tools (MLflow, Weights & Biases, BentoML) and containerization strategies (Docker, Kubernetes)
Understanding of data engineering and ingestion pipelines, with ability to interface with data lakes, feature stores, and streaming systems
Proven ability to work cross-functionally with Data Science, Data Platform, and Software Engineering teams, influencing decisions and driving alignment
Passion for AI and automation to solve real-world problems and improve operational workflows

Job Responsibility

Architect, build, own, and operate scalable ML infrastructure in cloud environments (e.g., AWS), optimizing for speed, observability, cost, and reproducibility
Create, support, and maintain core MLOps infrastructure (e.g., MLflow, feature store, experiment tracking, model registry), ensuring reliability, scalability, and long-term sustainability
Develop, evolve, and operate MLOps platforms and frameworks that standardize model deployment, versioning, drift detection, and lifecycle management at scale
Implement and continuously maintain end-to-end CI/CD pipelines for ML models using orchestration tools (e.g., Prefect, Airflow, Argo Workflows), ensuring robust testing, reproducibility, and traceability
Partner closely with Data Science, Sensor Intelligence, and Data Platform teams to operationalize and support model development, deployment, and monitoring workflows
Build, manage, and maintain both real-time and batch inference infrastructure, supporting diverse use cases from physiological analytics to personalized feedback loops for WHOOP members
Design, implement, and own automated observability tooling (e.g., for model latency, data drift, accuracy degradation), integrating metrics, logging, and alerting with existing platforms
Leverage AI-powered tools and automation to reduce operational overhead, enhance developer productivity, and accelerate model release cycles
Contribute to and maintain internal platform documentation, SDKs, and training materials, enabling self-service capabilities for model deployment and experimentation
Continuously evaluate and integrate emerging technologies and deployment strategies, influencing WHOOP’s roadmap for AI-driven platform efficiency, reliability, and scale

What we offer

equity
benefits

Fulltime

Engineering Manager, Infrastructure

As an Engineering Manager for the Infrastructure team, you’ll lead the engineers...

Location

Canada; United States

Salary:

195000.00 - 285000.00 USD / Year

Apollo.io

Expiration Date

Until further notice

Requirements

5+ years of hands-on software or infrastructure engineering experience
2+ years of experience leading teams of senior and staff-level engineers in platform, SRE, or infrastructure domains
Proven ability to design and operate large-scale distributed systems in cloud environments (preferably GCP or AWS)
Expertise with Kubernetes, Docker, Terraform, Ubuntu, and CI/CD pipelines
Familiarity with observability tools (Grafana, Prometheus, ELK, Datadog, NewRelic) and performance tuning
Strong grounding in networking, security, and reliability principles
Experience managing infrastructure costs, availability SLAs, and high-throughput systems at scale

Job Responsibility

Lead, coach, and grow a distributed team of high-impact Infrastructure Engineers
Partner with senior engineering leadership on strategic initiatives such as cloud migration, infrastructure scaling, platform reliability, and cost efficiency
Define and implement modern operational excellence practices, including SLOs, error budgets, incident reviews, and performance monitoring
Guide technical decision-making across key areas like Kubernetes, GCP, observability, networking, CI/CD, and IaC (Terraform, Ansible)
Collaborate with AI, Data, and Product Engineering teams to ensure infrastructure scalability for ML and AI-native workloads
Run effective 1:1s, career development conversations, and quarterly performance reviews
Support recruiting efforts to attract top engineering talent across time zones

What we offer

Equity
Company bonus or sales commissions/bonuses
401(k) plan
At least 10 paid holidays per year
Flex PTO
Parental leave
Employee assistance program and wellbeing benefits
Global travel coverage
Life/AD&D/STD/LTD insurance
FSA/HSA and medical, dental, and vision benefits

Fulltime

Cloud Engineer

Fyld is a Portuguese consulting company specializing in IT services. We bring hi...

Location

Portugal , Lisboa

Salary:

Not provided

Fyld

Expiration Date

Until further notice

Requirements

Degree in Computer Engineering or a similar
Significant experience with the three major public cloud platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)
Familiarity with the key services offered by each provider, including compute, storage, networking, databases, security, data analytics, artificial intelligence (AI), and machine learning (ML)
Relevant certifications in each of the three public clouds are highly desirable, such as AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect Expert, and Google Cloud Professional Cloud Architect
Experience with infrastructure automation tools such as Terraform, AWS CloudFormation, Azure Resource Manager (ARM), and Google Cloud Deployment Manager
Ability to write and maintain infrastructure-as-code templates to provision and manage cloud resources efficiently and at scale
Proficiency in container technologies such as Docker, and container orchestrators like Kubernetes
Experience in deploying, managing, and scaling containerized applications in public cloud environments
Knowledge of monitoring and logging tools such as AWS CloudWatch, Azure Monitor, Google Cloud Monitoring, and Stackdriver
Ability to configure and customize metrics, alerts, and dashboards to ensure the availability and performance of cloud services

Fulltime

Member of Technical Staff, Cloud Infrastructure

As a Software Engineer on our Cloud Infrastructure team, you'll be at the forefr...

Location

United States , New York, NY; San Mateo, CA; Redwood City, CA

Salary:

175000.00 - 220000.00 USD / Year

Fireworks AI

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
5+ years of experience designing and building backend infrastructure in cloud environments (e.g., AWS, GCP, Azure)
Proven experience in ML infrastructure and tooling (e.g., PyTorch, TensorFlow, Vertex AI, SageMaker, Kubernetes, etc.)
Strong software development skills in languages like Python, or C++
Deep understanding of distributed systems fundamentals: scheduling, orchestration, storage, networking, and compute optimization

Job Responsibility

Architect and build scalable, resilient, and high-performance backend infrastructure to support distributed training, inference, and data processing pipelines
Lead technical design discussions, mentor other engineers, and establish best practices for building and operating large-scale ML infrastructure
Design and implement core backend services (e.g., job schedulers, resource managers, autoscalers, model serving layers) with a focus on efficiency and low latency
Drive infrastructure optimization initiatives, including compute cost reduction, storage lifecycle management, and network performance tuning
Collaborate cross-functionally with ML, DevOps, and product teams to translate research and product needs into robust infrastructure solutions
Continuously evaluate and integrate cloud-native and open-source technologies (e.g., Kubernetes, Ray, Kubeflow, MLFlow) to enhance our platform’s capabilities and reliability
Own end-to-end systems from design to deployment and observability, with a strong emphasis on reliability, fault tolerance, and operational excellence

What we offer

Meaningful equity in a fast-growing startup
Competitive salary
Comprehensive benefits package

Fulltime

Senior Machine Learning Engineer (Infrastructure)

We are looking for an experienced MLOps Engineer to join our team as a Senior Ma...

Location

United States , Boston

Salary:

152800.00 - 224100.00 USD / Year

SimpliSafe

Expiration Date

Until further notice

Requirements

5+ years of experience in software engineering, data engineering, or a related field, with at least 3 years focused on MLOps or ML infrastructure
Deep hands-on experience with AWS or similar public clouds, including compute, networking, container orchestration, and observability stacks
Hands-on experience with: CI/CD pipelines, Docker
Kubernetes
Infrastructure-as-code tools (e.g., Terraform, Cloud Formation)
Proficiency in programming languages like Python, and familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch)
Solid understanding of ML lifecycle management, including experiment tracking, versioning, and monitoring
LLM application development, including prompt engineering and evaluation
Strong communication skills for partnering with cross-functional technical and non-technical teams

Job Responsibility

Lead the architecture, deployment, and optimization of scalable ML model serving systems for real-time and batch use cases
Collaborate with data scientists, engineers, and stakeholders to operationalize ML models
Develop CI/CD pipelines for ML models enabling rapid, safe, and consistent model releases
Design, implement, and own comprehensive production monitoring for ML models/systems
Manage cloud infrastructure, primarily in AWS or other major public clouds, to support ML workloads
Drive best practices in model versioning, observability, reproducibility, and deployment reliability
Serve in an on-call rotation as a first responder for software owned by your team

What we offer

A mission- and values-driven culture and a safe, inclusive environment where you can build, grow and thrive
A comprehensive total rewards package that supports your wellness and provides security for SimpliSafers and their families
Free SimpliSafe system and professional monitoring for your home
Employee Resource Groups (ERGs) that bring people together, give opportunities to network, mentor and develop, and advocate for change
Participation in our annual bonus program, equity, and other forms of compensation
A full range of medical, retirement, and lifestyle benefits

Fulltime

Senior ML Data Engineer

As a Senior Data Engineer, you will play a pivotal role in our AI/ML workstream,...

Location

Salary:

Not provided

Awin Global

Expiration Date

Until further notice

Requirements

Bachelor or Master’s degree in data science, data engineering, Computer Science with focus on math and statistics / Master’s degree is preferred
At least 5 years experience as AI/ML data engineer undertaking above task and accountabilities
Strong foundation in computer science principes and statistical methods
Strong experience with cloud technology (AWS or Azure)
Strong experience with creation of data ingestion pipeline and ET process
Strong knowledge of big data tool such as Spark, Databricks and Python
Strong understanding of common machine learning techniques and frameworks (e.g. mlflow)
Strong knowledge of Natural language processing (NPL) concepts
Strong knowledge of scrum practices and agile mindset

Job Responsibility

Design and maintain scalable data pipelines and storage systems for both agentic and traditional ML workloads
Productionise LLM- and agent-based workflows, ensuring reliability, observability, and performance
Build and maintain feature stores, vector/embedding stores, and core data assets for ML
Develop and manage end-to-end traditional ML pipelines: data prep, training, validation, deployment, and monitoring
Implement data quality checks, drift detection, and automated retraining processes
Optimise cost, latency, and performance across all AI/ML infrastructure
Collaborate with data scientists and engineers to deliver production-ready ML and AI systems
Ensure AI/ML systems meet governance, security, and compliance requirements
Mentor teams and drive innovation across both agentic and classical ML engineering practices
Participate in team meetings and contribute to project planning and strategy discussions

What we offer

Flexi-Week and Work-Life Balance: We prioritise your mental health and well-being, offering you a flexible four-day Flexi-Week at full pay and with no reduction to your annual holiday allowance. We also offer a variety of different paid special leaves as well as volunteer days
Remote Working Allowance: You will receive a monthly allowance to cover part of your running costs. In addition, we will support you in setting up your remote workspace appropriately
Pension: Awin offers access to an additional pension insurance to all employees in Germany
Flexi-Office: We offer an international culture and flexibility through our Flexi-Office and hybrid/remote work possibilities to work across Awin regions
Development: We’ve built our extensive training suite Awin Academy to cover a wide range of skills that nurture you professionally and personally, with trainings conveniently packaged together to support your overall development
Appreciation: Thank and reward colleagues by sending them a voucher through our peer-to-peer program

Software Engineer, Infrastructure

As a Software Engineer on our Infrastructure team, you will help design and buil...

Location

United States , New York; San Mateo; Redwood City

Salary:

140000.00 - 150000.00 USD / Year

Fireworks AI

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
Strong programming skills in Python, C++, or a similar language
Solid understanding of computer systems concepts such as networking, storage, and distributed computing
Familiarity with cloud platforms like AWS, GCP, or Azure, and containerization tools like Docker or Kubernetes
Knowledge and interest in cloud infrastructure, distributed systems, and machine learning

Job Responsibility

Contribute to the design and development of scalable backend infrastructure that supports distributed training, inference, and data pipelines
Build and maintain core backend services such as job schedulers, autoscalers, resource managers, and model serving systems
Support performance optimization, cost efficiency, and reliability improvements across compute, storage, and networking layers
Collaborate with ML, DevOps, and product teams to translate research and product needs into infrastructure solutions
Learn and apply modern cloud technologies including Kubernetes, Ray, Kubeflow, and MLFlow
Participate in code reviews, technical discussions, and continuous integration and deployment processes

What we offer

Meaningful equity in a fast-growing startup
Competitive salary and comprehensive benefits package

Fulltime

ML Ops Engineer

As an MLOps Engineer, you will be responsible for building, maintaining, and opt...

Location

India , Hyderabad

Salary:

Not provided

NStarX

Expiration Date

Until further notice

Requirements

4 to 10 years of experience in MLOps, DevOps, or ML Engineering
Strong proficiency with cloud platforms such as AWS, Azure, or GCP
Experience with containerization and orchestration tools like Docker and Kubernetes
Hands-on experience with ML model deployment, monitoring, and scaling
Proficiency with CI/CD tools such as Jenkins or GitLab CI
Familiarity with data versioning and management tools such as DVC
Strong coding skills in Python with knowledge of ML libraries like TensorFlow or PyTorch
Strong problem-solving skills and ability to work in a collaborative environment
Effective communication skills for cross-functional teamwork

Job Responsibility

Develop and manage infrastructure for end-to-end ML workflows including model training, deployment, monitoring, and maintenance
Implement CI/CD pipelines for ML models and data workflows
Collaborate with cross-functional teams to build scalable and robust ML infrastructure on cloud and on-premises environments
Monitor and optimize model performance and infrastructure to ensure efficient resource usage
Manage data versioning and model versioning across multiple environments
Implement security, governance, and compliance protocols in ML deployment and data pipelines
Support troubleshooting, debugging, and incident management for ML infrastructure issues

What we offer

Competitive compensation
Opportunity to work with a dynamic team on cutting-edge AI and ML solutions
Professional growth and development opportunities

Fulltime

Cloud Infrastructure Engineer – AWS & ML Tooling

Zelis

Location:
United States

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Nice to have:

Additional Information:

Job Posted:
February 20, 2026

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