AI Native Operations Expert

About the Role

We are looking for passionate and driven interns across multiple technology domains including Frontend Development, Backend Development, DevOps, AI/ML, and Data Engineering. This internship offers hands-on experience in real-world projects, collaboration with cross-functional teams, and exposure to modern tools and technologies. 

Domains & Responsibilities 

Frontend Development 

• Build responsive and user-friendly web interfaces  
• Translate UI/UX designs into functional applications  
• Optimize performance and ensure cross-browser compatibility  

 Backend Development 

• Develop APIs and server-side logic  
• Work with databases and data storage solutions  
• Ensure application security and performance  

 DevOps 

• Assist in CI/CD pipeline setup and automation  
• Manage deployments and cloud infrastructure  
• Monitor system performance and reliability  

 AI / Machine Learning 

• Develop and train ML models  
• Work on NLP, automation, or AI-driven features  
• Analyze datasets and evaluate model performance  

 Data Engineering 

• Build and maintain data pipelines (ETL/ELT)  
• Ensure data quality and availability  
• Work with large datasets and optimize data workflows  

 Required Skills (Any Domain) 

• Frontend: HTML, CSS, JavaScript, React/Vue/Angular  
• Backend: Node.js / Python / Java / PHP, APIs, databases  
• DevOps: Linux, Git, CI/CD basics, cloud fundamentals  
• AI/ML: Python, ML basics, TensorFlow/PyTorch/Scikit-learn  
• Data Engineering: SQL, Python, data processing concepts  

 Good to Have 

• Knowledge of Git and version control  
• Basic understanding of cloud platforms (AWS/Azure/GCP)  
• Problem-solving mindset and willingness to learn  
• Exposure to real-world or academic projects  

 Who Should Apply 

• Students or recent graduates in Computer Science, IT, or related fields  
• Candidates with strong interest in any of the above domains  
• Self-learners with project experience are highly encouraged  

 Internship Details 

• Duration: 3–6 months  
• Mode: Remote  
• Certificate + PPO (Pre-Placement Offer) based on performance  

 What You’ll Gain 

• Hands-on experience with real projects  
• Mentorship from experienced professionals  
• Exposure to industry tools and workflows  
• Opportunity to convert to a full-time role 

We are seeking an experienced MLOps Architect who can drive end-to-end implementation of the proposal being prepared for the initiative and also contribute broadly across other enterprise AI/ML programs. This role demands a strong architectural mindset, hands-on technical depth, and the ability to design scalable, cloud-native machine learning operations across traditional ML and modern LLM workflows.

The ideal candidate will bring experience with SageMaker-based MLOps pipelines, evaluation of equivalent tooling stacks, hybrid MLOps/LLMOps automation, CI/CD orchestration, governance, and production-grade scalability patterns.

Must have skills & Qualifications:

• 8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure.
• Strong expertise in AWS cloud-native ML stack, including: SageMaker(primary), ECS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent)
• Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon.
• Deep understanding of model lifecycle management (feature engineering->training -> registry -> deployment -> monitoring).
• Experience implementing or supporting LLMOps pipelines, including: prompt versioning, evaluation metrics, automation frameworks.
• Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
• Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor).
• Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
• Strong SQL and data transformation experience using Snowflake, Databricks, Spark.
• Experience with feature engineering pipelines and Feature Store management.
• Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility.
• Hands-on experience with Bedrock, OpenAI, Anthropic, or Llama models.
• Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana.
• Strong foundation in Python and cloud-native development patterns.
• Solid understanding of security best practices, IAM, secrets management, and artifact governance.

Good to have skills:

• Experience with vector databases, RAG pipelines, or multi-agent AI systems.
• Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK).
• Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments.
• Familiarity with Observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry).
• Knowledge of Lakehouse (Delta/Iceberg/Hudi) architecture.
• Ability to translate business goals into scalable AI/ML platform designs.
• Strong communication and cross-team collaboration skills.
• Ability to guide engineering teams through technical uncertainty and design choices.

Key Responsibilities:

• Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap.
• Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation.
• Build container-oriented ML platforms (EKS-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, SageMaker, MLflow, Airflow, etc.).
• Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems.
• Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks.
• Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.
• Collaborate with cross-functional teams — data engineering, platform, DevOps, and client stakeholders — to deliver production-ready ML solutions.
• Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling cloud services, Kubernetes workloads, and MLOps tools.
• Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms.
• Mentor engineers and provide guidance on modern MLOps tools, platform capabilities, and best practices. 

Amura’s Vision 

We believe that the most under-appreciated route to releasing untapped human potential is to build a healthier body, and through which a better brain. This allows us to do more of everything that is important to each one of us.

Billions of healthier brains, sitting in healthier bodies, can take up more complex problems that defy solutions today, including many existential threats, and solve them in just a few decades.

Billions of healthier brains will make the world richer beyond what we can imagine today. The surplus wealth, combined with better human capabilities, will lead us to a new renaissance, giving us a richer and more beautiful culture.

These healthier brains will be equipped with deeper intellect, be less acrimonious, more magnanimous, and have a kinder outlook on the world, resulting in a world that is better than any previous time.

We find this vision of the future exhilarating. Our hopes and dreams are to create this future as quickly as possible and ensure that it is widely distributed and optimized to maximize all forms of human excellence. 

Role Overview 

We are looking for a highly skilled Senior DevOps Engineer (AI-Native Infrastructure & Platform Engineering) with deep expertise in AWS cloud infrastructure, automation, AI infrastructure operations, and modern DevOps/SRE practices.

This role goes beyond traditional DevOps and requires a seasoned specialist capable of building and operating AI-ready infrastructure platforms that support high-throughput APIs, LLM/AI workloads, GPU-based compute, data-intensive systems, real-time inference pipelines, and scalable ML platforms.

You will be responsible for architecting, automating, securing, and optimizing highly scalable and cost-efficient cloud environments that enable high-velocity engineering and AI teams. This is an ideal position for someone who combines technical ownership, an automation-first mindset, and a passion for developer productivity and platform reliability. 

Key Responsibilities 

Cloud Infrastructure & Platform Engineering (AWS) 

• Architect, deploy, and manage highly scalable and secure infrastructure on AWS. Design cloud platforms supporting AI/ML workloads, data pipelines, real-time APIs, and high-concurrency backend systems.
• Hands-on expertise with key AWS services including EC2, ECS/EKS, Lambda, RDS, DynamoDB, S3, VPC, CloudFront, IAM, CloudWatch, and GPU-enabled instances.
• Build and maintain Infrastructure-as-Code (IaC) using Terraform, CloudFormation, or AWS CDK.
• Design multi-AZ and multi-region architectures for high availability and disaster recovery (HA/DR).
• Build reusable platform templates and shared infrastructure modules. 

AI/ML Infrastructure & MLOps 

• Build and maintain infrastructure for LLM applications, AI inference workloads, model serving platforms, vector databases, and feature stores.
• Support GPU-based workloads and optimize compute/storage usage.
• Enable scalable deployment patterns for AI applications using Kubernetes/EKS. Collaborate with Data Science and ML Engineering teams on model deployment, training/tuning of models, CI/CD for ML systems, experiment environments, and reproducibility.
• Support orchestration and deployment of AI workflows and inference services while implementing observability and reliability for AI pipelines. 

CI/CD, Automation & Developer Productivity 

• Build and maintain CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline.
• Automate deployments, environment provisioning, and release workflows.
• Build self-service developer platforms, preview environments, and reusable deployment workflows to improve developer productivity.
• Implement automated patching, scaling, backups, cleanup workflows, and drift detection. 

Containers, Kubernetes & Platform Reliability

• Manage Docker-based environments, containerized applications, and optimize workloads using Kubernetes (EKS) or ECS/Fargate.
• Manage autoscaling, cluster health, node pools, ingress, service mesh, and workload isolation.
• Optimize infrastructure for performance, resilience, and cost-efficiency.
• Implement progressive deployment strategies including blue/green, canary, and rolling deployments. 

Observability, Incident Response & SRE Practices

• Implement observability stacks using CloudWatch, Prometheus, Grafana, ELK, Datadog, OpenTelemetry, or New Relic.
• Build actionable dashboards and intelligent alerting systems while defining and tracking SLIs, SLOs, and SLAs.
• Lead incident response, root cause analysis, and blameless postmortems to reduce operational toil and improve MTTR.

FinOps, Cost Governance & Security

• Continuously monitor and optimize cloud costs (compute utilization, storage lifecycle, GPU usage, and data transfer) using AWS Cost Explorer, Budgets, Trusted Advisor, CloudHealth, or Kubecost.
• Implement AWS security best practices for IAM, VPCs, security groups, NACLs, encryption, and manage secrets using KMS, SSM Parameter Store, or Vault.
• Build secure CI/CD pipelines with automated security checks, least-privilege access, audit logging, and ensure compliance readiness for ISO 27001, SOC2, and GDPR.

Collaboration, Leadership & Platform Culture

• Work closely with engineering, AI/ML, QA, product, and operations teams to drive a DevOps, SRE, GitOps, and automation-first culture.
• Mentor junior DevOps and Platform Engineers while creating and maintaining detailed runbooks, architecture diagrams, and platform documentation.

Skills & Qualifications

Must-Have:

• 7+ years of experience in DevOps, SRE, Platform Engineering, or Cloud Infrastructure Engineering.
• Strong expertise in AWS cloud architecture, services, and deep understanding of Kubernetes (EKS), containers, and cloud-native systems.
• Strong Infrastructure-as-Code expertise using Terraform, CloudFormation, or CDK. Strong Linux administration, networking, DNS, routing, and load balancing knowledge. Strong scripting/programming experience in Python, Bash, or Go (preferred). Experience with CI/CD automation, GitOps workflows, and observability platforms supporting scalable production systems.

Preferred / Nice-to-Have:

• Experience with AI/ML infrastructure, MLOps, model serving, vector databases, GPU orchestration, and inference optimization.
• Familiarity with Kafka, Redis, SQS, and event-driven systems.
• Exposure to platform engineering, internal developer platforms, and tools like ArgoCD, Flux, Helm, and OpenTelemetry.
• AWS Certifications: Solutions Architect, DevOps Engineer, or SysOps Administrator. Knowledge of distributed systems and large-scale platform operations. 

Preferred / Nice-to-Have:

• Experience with AI/ML infrastructure, MLOps, model serving, vector databases, GPU orchestration, and inference optimization.
• Familiarity with Kafka, Redis, SQS, and event-driven systems.
• Exposure to platform engineering, internal developer platforms, and tools like ArgoCD, Flux, Helm, and OpenTelemetry.
• AWS Certifications: Solutions Architect, DevOps Engineer, or SysOps Administrator. Knowledge of distributed systems and large-scale platform operations. 

Here are answers to some questions you may have

Where is your office?

Chennai (Velachery)

Work Model

Work from Office – because great stories are built in person!

Do you have an online presence?

https://amura.ai (we are @AmuraHealth on all social media)

Review Criteria

• Strong DevOps /Cloud Engineer Profiles
• Must have 3+ years of experience as a DevOps / Cloud Engineer
• Must have strong expertise in cloud platforms – AWS / Azure / GCP (any one or more)
• Must have strong hands-on experience in Linux administration and system management
• Must have hands-on experience with containerization and orchestration tools such as Docker and Kubernetes
• Must have experience in building and optimizing CI/CD pipelines using tools like GitHub Actions, GitLab CI, or Jenkins
• Must have hands-on experience with Infrastructure-as-Code tools such as Terraform, Ansible, or CloudFormation
• Must be proficient in scripting languages such as Python or Bash for automation
• Must have experience with monitoring and alerting tools like Prometheus, Grafana, ELK, or CloudWatch
• Top tier Product-based company (B2B Enterprise SaaS preferred)

Preferred

• Experience in multi-tenant SaaS infrastructure scaling.
• Exposure to AI/ML pipeline deployments or iPaaS / reverse ETL connectors.

Role & Responsibilities

We are seeking a DevOps Engineer to design, build, and maintain scalable, secure, and resilient infrastructure for our SaaS platform and AI-driven products. The role will focus on cloud infrastructure, CI/CD pipelines, container orchestration, monitoring, and security automation, enabling rapid and reliable software delivery.

Key Responsibilities:

• Design, implement, and manage cloud-native infrastructure (AWS/Azure/GCP).
• Build and optimize CI/CD pipelines to support rapid release cycles.
• Manage containerization & orchestration (Docker, Kubernetes).
• Own infrastructure-as-code (Terraform, Ansible, CloudFormation).
• Set up and maintain monitoring & alerting frameworks (Prometheus, Grafana, ELK, etc.).
• Drive cloud security automation (IAM, SSL, secrets management).
• Partner with engineering teams to embed DevOps into SDLC.
• Troubleshoot production issues and drive incident response.
• Support multi-tenant SaaS scaling strategies.

Ideal Candidate

• 3–6 years' experience as DevOps/Cloud Engineer in SaaS or enterprise environments.
• Strong expertise in AWS, Azure, or GCP.
• Strong expertise in LINUX Administration.
• Hands-on with Kubernetes, Docker, CI/CD tools (GitHub Actions, GitLab, Jenkins).
• Proficient in Terraform/Ansible/CloudFormation.
• Strong scripting skills (Python, Bash).
• Experience with monitoring stacks (Prometheus, Grafana, ELK, CloudWatch).
• Strong grasp of cloud security best practices.

The DevOps Engineer will play a critical role in operationalizing artificial intelligence across Bell Techlogix client environments. This role focuses on building and supporting cloud infrastructure, CI/CD pipelines, and automation frameworks that power AI and machine learning workloads. The ideal candidate has experience supporting AI platforms such as Azure AI, Azure Machine Learning, Azure OpenAI, and ServiceNow or conversational AI platforms, and understands the operational requirements of production AI systems, including reliability, scalability, and security. 

Key Responsibilities 

•Design, build, and operate cloud infrastructure and platform services that support AI and machine learning workloads in production, SLA-driven managed services environments 

•Implement CI/CD and MLOps pipelines to enable automated training, testing, deployment, and rollback of AI and ML models 

•Develop and maintain Infrastructure as Code to provision AI-ready environments consistently across dev/test/prod 

•Support AI platform operations including monitoring model health, pipeline execution, compute utilization, and data dependencies 

•Partner with Machine Learning Engineers and Data Engineers to standardize deployment patterns for AI services and LLM-based solutions 

•Enable secure and scalable AI integrations using APIs, messaging, and event-driven architectures 

•Implement observability solutions for AI platforms, including logging, metrics, alerting, and drift detection integrations 

•Troubleshoot AI platform incidents, perform root cause analysis, and implement remediation to improve reliability and automation coverage 

•Apply security best practices for AI environments including secrets management, identity and access controls, network isolation, and policy enforcement 

•Support AI-driven automation use cases across platforms such as Microsoft Copilot, ServiceNow, and conversational AI tools 

•Collaborate with service desk, security, and architecture teams to continuously improve AI service delivery and operational maturity 

Required Qualifications 

•Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience 

•5+ years of experience in DevOps, cloud engineering, or platform operations, with exposure to AI or data workloads 

•Hands-on experience with Microsoft Azure, including compute, networking, storage, and monitoring services 

•Experience building CI/CD pipelines using Azure DevOps, GitHub Actions, or similar tools 

•Working knowledge of Infrastructure as Code (Terraform and/or Bicep/ARM) 

•Scripting experience using PowerShell and/or Python 

•Experience supporting production platforms with incident management, change control, and root cause analysis 

•Understanding of cloud security fundamentals and enterprise governance requirements 

Preferred Qualifications 

•Experience with Azure Machine Learning, Azure AI Services, Azure OpenAI, or MLOps frameworks 

•Exposure to containerization and orchestration technologies (Docker, Kubernetes, AKS) 

•Experience supporting data pipelines or feature stores used by machine learning systems 

•Familiarity with ServiceNow, AI-driven ITSM workflows, or automation platforms 

•Experience with observability tools 

•Knowledge of Responsible AI, data governance, and compliance considerations for AI systems 

•Relevant certifications (Microsoft Azure Administrator, Azure DevOps Engineer, Azure AI Engineer)

We are looking for a passionate DevOps Engineer who can support deployment and monitor our Production, QE, and Staging environments performance. Applicants should have a strong understanding of UNIX internals and should be able to clearly articulate how it works. Knowledge of shell scripting & security aspects is a must. Any experience with infrastructure as code is a big plus. The key responsibility of the role is to manage deployments, security, and support of business solutions. Having experience in database applications like Postgres, ELK, NodeJS, NextJS & Ruby on Rails is a huge plus. At VakilSearch. Experience doesn't matter, passion to produce change matters

Responsibilities and Accountabilities:

• As part of the DevOps team, you will be responsible for configuration, optimization, documentation, and support of the infra components of VakilSearch’s product which are hosted in cloud services & on-prem facility
• Design, build tools and framework that support deploying and managing our platform & Exploring new tools, technologies, and processes to improve speed, efficiency, and scalability
• Support and troubleshoot scalability, high availability, performance, monitoring, backup, and restore of different Env 
• Manage resources in a cost-effective, innovative manner including assisting subordinates ineffective use of resources and tools
• Resolve incidents as escalated from Monitoring tools and Business Development Team
• Implement and follow security guidelines, both policy and technology to protect our data
• Identify root cause for issues and develop long-term solutions to fix recurring issues and Document it
• Strong in performing production operation activities even at night times if required
• Ability to automate [Scripts] recurring tasks to increase velocity and quality
• Ability to manage and deliver multiple project phases at the same time

I Qualification(s): 

• Experience in working with Linux Server, DevOps tools, and Orchestration tools 
• Linux, AWS, GCP, Azure, CompTIA+, and any other certification are a value-add 

II Experience Required in DevOps Aspects:

• Length of Experience: Minimum 1-4 years of experience
• Nature of Experience: 
• Experience in Cloud deployments, Linux administration[ Kernel Tuning is a value add ], Linux clustering, AWS, virtualization, and networking concepts [ Azure, GCP value add ]
• Experience in deployment solutions CI/CD like Jenkins, GitHub Actions [ Release Management is a value add ]
• Hands-on experience in any of the configuration management IaC tools like Chef, Terraform, and CloudFormation [ Ansible & Puppet is a value add ]
• Administration, Configuring and utilizing Monitoring and Alerting tools like Prometheus, Grafana, Loki, ELK, Zabbix, Datadog, etc
• Experience with Containerization and orchestration tools like Docker, and Kubernetes [ Docker swarm is a value add ]Good Scripting skills in at least one interpreted language - Shell/bash scripting or Ruby/Python/Perl
• Experience in Database applications like PostgreSQL, MongoDB & MySQL [DataOps]
• Good at Version Control & source code management systems like GitHub, GIT
• Experience in Serverless [ Lambda/GCP cloud function/Azure function ]
• Experience in Web Server Nginx, and Apache
• Knowledge in Redis, RabbitMQ, ELK, REST API [ MLOps Tools is a value add ]
• Knowledge in Puma, Unicorn, Gunicorn & Yarn
• Hands-on VMWare ESXi/Xencenter deployments is a value add
• Experience in Implementing and troubleshooting TCP/IP networks, VPN, Load Balancing & Web application firewalls
• Deploying, Configuring, and Maintaining Linux server systems ON premises and off-premises
• Code Quality like SonarQube is a value-add
• Test Automation like Selenium, JMeter, and JUnit is a value-add
• Experience in Heroku and OpenStack is a value-add 
• Experience in Identifying Inbound and Outbound Threats and resolving it
• Knowledge of CVE & applying the patches for OS, Ruby gems, Node, and Python packages  
• Documenting the Security fix for future use
• Establish cross-team collaboration with security built into the software development lifecycle 
• Forensics and Root Cause Analysis skills are mandatory 
• Weekly Sanity Checks of the on-prem and off-prem environment 

III Skill Set & Personality Traits required:

• An understanding of programming languages such as Ruby, NodeJS, ReactJS, Perl, Java, Python, and PHP
• Good written and verbal communication skills to facilitate efficient and effective interaction with peers, partners, vendors, and customers

IV Age Group: 21 – 36 Years

V Cost to the Company: As per industry standards

Lead DevSecOps Engineer

Location: Pune, India (In-office) | Experience: 3–5 years | Type: Full-time

Apply here → https://lnk.ink/CLqe2

About FlytBase:

FlytBase is a Physical AI platform powering autonomous drones and robots across industrial sites. Our software enables 24/7 operations in critical infrastructure like solar farms, ports, oil refineries, and more.

We're building intelligent autonomy — not just automation — and security is core to that vision.

What You’ll Own

You’ll be leading and building the backbone of our AI-native drone orchestration platform — used by global industrial giants for autonomous operations.

Expect to:

• Design and manage multi-region, multi-cloud infrastructure (AWS, Kubernetes, Terraform, Docker)
• Own infrastructure provisioning through GitOps, Ansible, Helm, and IaC
• Set up observability stacks (Prometheus, Grafana) and write custom alerting rules
• Build for Zero Trust security — logs, secrets, audits, access policies
• Lead incident response, postmortems, and playbooks to reduce MTTR
• Automate and secure CI/CD pipelines with SAST, DAST, image hardening
• Script your way out of toil using Python, Bash, or LLM-based agents
• Work alongside dev, platform, and product teams to ship secure, scalable systems

What We’re Looking For:

You’ve probably done a lot of this already:

• 3–5+ years in DevOps / DevSecOps for high-availability SaaS or product infra
• Hands-on with Kubernetes, Terraform, Docker, and cloud-native tooling
• Strong in Linux internals, OS hardening, and network security
• Built and owned CI/CD pipelines, IaC, and automated releases
• Written scripts (Python/Bash) that saved your team hours
• Familiar with SOC 2, ISO 27001, threat detection, and compliance work

Bonus if you’ve:

• Played with LLMs or AI agents to streamline ops and Built bots that monitor, patch, or auto-deploy.

What It Means to Be a Flyter

• AI-native instincts: You don’t just use AI — you think in it. Your terminal window has a co-pilot.
• Ownership without oversight: You own outcomes, not tasks. No one micromanages you here.
• Joy in complexity: Security + infra + scale = your happy place.
• Radical candor: You give and receive sharp feedback early — and grow faster because of it.
• Loops over lines: we prioritize continuous feedback, iteration, and learning over one-way execution or rigid, linear planning.
• H3: Happy. Healthy. High-Performing. We believe long-term performance stems from an environment where you feel emotionally fulfilled, physically well, and deeply motivated.
• Systems > Heroics: We value well-designed, repeatable systems over last-minute firefighting or one-off effort.

Perks:

▪ Unlimited leave & flexible hours

▪ Top-tier health coverage

▪ Budget for AI tools, courses

▪ International deployments

▪ ESOPs and high-agency team culture

Apply Here- https://lnk.ink/CLqe2

Key Responsibilities:-

• Collaborate with Data Scientists to test and scale new algorithms through pilots and later industrialize the solutions at scale to the comprehensive fashion network of the Group

• Influence, build and maintain the large-scale data infrastructure required for the AI projects, and integrate with external IT infrastructure/service to provide an e2e solution

• Leverage an understanding of software architecture and software design patterns to write scalable, maintainable, well-designed and future-proof code

• Design, develop and maintain the framework for the analytical pipeline

• Develop common components to address pain points in machine learning projects, like model lifecycle management, feature store and data quality evaluation

• Provide input and help implement framework and tools to improve data quality

• Work in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, designers, product managers and others to build the AI ecosystem within the Group

• Deliver on time, demonstrating a strong commitment to deliver on the team mission and agreed backlog

We are looking for a full-time remote DevOps Engineer who has worked with CI/CD automation, big data pipelines and Cloud Infrastructure, to solve complex technical challenges at scale that will reshape the healthcare industry for generations. You will get the opportunity to be involved in the latest tech in big data engineering, novel machine learning pipelines and highly scalable backend development. The successful candidates will be working in a team of highly skilled and experienced developers, data scientists and CTO.

Job Requirements

• Experience deploying, automating, maintaining, and improving complex services and pipelines • Strong understanding of DevOps tools/process/methodologies
• Experience with AWS Cloud Formation and AWS CLI is essential
• The ability to work to project deadlines efficiently and with minimum guidance
• A positive attitude and enjoys working within a global distributed team

Skills

• Highly proficient working with CI/CD and automating infrastructure provisioning
• Deep understanding of AWS Cloud platform and hands on experience setting up and maintaining with large scale implementations
• Experience with JavaScript/TypeScript, Node, Python and Bash/Shell Scripting
• Hands on experience with Docker and container orchestration
• Experience setting up and maintaining big data pipelines, Serverless stacks and containers infrastructure
• An interest in healthcare and medical sectors
• Technical degree with 4 plus years’ infrastructure and automation experience