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 looking for a highly skilled and experienced Senior AIOps / MLOps Engineer with strong expertise in Azure Cloud, automation, platform engineering, CI/CD, observability, and enterprise-scale cloud operations.

The ideal candidate should have hands-on experience in designing, implementing, and managing modern cloud-native platforms with focus on AI/ML operationalization, DevOps automation, monitoring, reliability, and infrastructure modernization.

Required Experience

• 6 – 10 Years of overall IT experience
• Strong experience in AIOps / MLOps / DevOps engineering
• Hands-on enterprise experience in Azure Cloud platform engineering

Key Responsibilities

AIOps / MLOps

• Design and implement scalable enterprise-grade AIOps and MLOps platforms across cloud environments.
• Ensure AI platform reliability, governance, security, and model performance optimization.
• Implement LLM/AI model versioning, experiment tracking, drift detection, observability, and operational health monitoring frameworks.
• Collaborate with Data Science, DevOps, Cloud, and Application teams to accelerate AI/ML adoption and platform modernization.
• Develop automation frameworks for AI/ML pipelines, infrastructure provisioning, and operational workflows.
• Lead continuous improvement, automation, and standardization efforts across AI/ML operational ecosystems
• Mentor engineering teams and promote AIOps/MLOps best practices, innovation, and engineering excellence
• Strong Knowledge on embeddings, tokenization, vector databases, and AI/ML model training concepts

Preferred Skills

• Python, MLflow, Model Registry, Experiment Tracking
• Azure DevOps & Azure Cloud
• Azure Machine Learning
• LLMOps / Generative AI operationalization
• AI model deployment and lifecycle management
• AI Gateway and Model Serving architectures
• Azure OpenAI & Azure AI Foundry
• MCP Server implementation and configuration  
• CI/CD Automation & AKS

Soft Skills

• Strong communication and stakeholder management
• Good troubleshooting and problem-solving skills
• Ability to work independently and drive ownership
• Strong collaboration and documentation skills

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)

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.

ROLE & RESPONSIBILITIES:

We are hiring a Senior DevSecOps / Security Engineer with 8+ years of experience securing AWS cloud, on-prem infrastructure, DevOps platforms, MLOps environments, CI/CD pipelines, container orchestration, and data/ML platforms. This role is responsible for creating and maintaining a unified security posture across all systems used by DevOps and MLOps teams — including AWS, Kubernetes, EMR, MWAA, Spark, Docker, GitOps, observability tools, and network infrastructure.

KEY RESPONSIBILITIES:

1.     Cloud Security (AWS)-

• Secure all AWS resources consumed by DevOps/MLOps/Data Science: EC2, EKS, ECS, EMR, MWAA, S3, RDS, Redshift, Lambda, CloudFront, Glue, Athena, Kinesis, Transit Gateway, VPC Peering.
• Implement IAM least privilege, SCPs, KMS, Secrets Manager, SSO & identity governance.
• Configure AWS-native security: WAF, Shield, GuardDuty, Inspector, Macie, CloudTrail, Config, Security Hub.
• Harden VPC architecture, subnets, routing, SG/NACLs, multi-account environments.
• Ensure encryption of data at rest/in transit across all cloud services.

2.     DevOps Security (IaC, CI/CD, Kubernetes, Linux)-

Infrastructure as Code & Automation Security:

• Secure Terraform, CloudFormation, Ansible with policy-as-code (OPA, Checkov, tfsec).
• Enforce misconfiguration scanning and automated remediation.

CI/CD Security:

• Secure Jenkins, GitHub, GitLab pipelines with SAST, DAST, SCA, secrets scanning, image scanning.
• Implement secure build, artifact signing, and deployment workflows.

Containers & Kubernetes:

• Harden Docker images, private registries, runtime policies.
• Enforce EKS security: RBAC, IRSA, PSP/PSS, network policies, runtime monitoring.
• Apply CIS Benchmarks for Kubernetes and Linux.

Monitoring & Reliability:

• Secure observability stack: Grafana, CloudWatch, logging, alerting, anomaly detection.
• Ensure audit logging across cloud/platform layers.

3.     MLOps Security (Airflow, EMR, Spark, Data Platforms, ML Pipelines)-

Pipeline & Workflow Security:

• Secure Airflow/MWAA connections, secrets, DAGs, execution environments.
• Harden EMR, Spark jobs, Glue jobs, IAM roles, S3 buckets, encryption, and access policies.

ML Platform Security:

• Secure Jupyter/JupyterHub environments, containerized ML workspaces, and experiment tracking systems.
• Control model access, artifact protection, model registry security, and ML metadata integrity.

Data Security:

• Secure ETL/ML data flows across S3, Redshift, RDS, Glue, Kinesis.
• Enforce data versioning security, lineage tracking, PII protection, and access governance.

ML Observability:

• Implement drift detection (data drift/model drift), feature monitoring, audit logging.
• Integrate ML monitoring with Grafana/Prometheus/CloudWatch.

4.     Network & Endpoint Security-

• Manage firewall policies, VPN, IDS/IPS, endpoint protection, secure LAN/WAN, Zero Trust principles.
• Conduct vulnerability assessments, penetration test coordination, and network segmentation.
• Secure remote workforce connectivity and internal office networks.

5.     Threat Detection, Incident Response & Compliance-

• Centralize log management (CloudWatch, OpenSearch/ELK, SIEM).
• Build security alerts, automated threat detection, and incident workflows.
• Lead incident containment, forensics, RCA, and remediation.
• Ensure compliance with ISO 27001, SOC 2, GDPR, HIPAA (as applicable).
• Maintain security policies, procedures, RRPs (Runbooks), and audits.

IDEAL CANDIDATE:

• 8+ years in DevSecOps, Cloud Security, Platform Security, or equivalent.
• Proven ability securing AWS cloud ecosystems (IAM, EKS, EMR, MWAA, VPC, WAF, GuardDuty, KMS, Inspector, Macie).
• Strong hands-on experience with Docker, Kubernetes (EKS), CI/CD tools, and Infrastructure-as-Code.
• Experience securing ML platforms, data pipelines, and MLOps systems (Airflow/MWAA, Spark/EMR).
• Strong Linux security (CIS hardening, auditing, intrusion detection).
• Proficiency in Python, Bash, and automation/scripting.
• Excellent knowledge of SIEM, observability, threat detection, monitoring systems.
• Understanding of microservices, API security, serverless security.
• Strong understanding of vulnerability management, penetration testing practices, and remediation plans.

EDUCATION:

• Master’s degree in Cybersecurity, Computer Science, Information Technology, or related field.
• Relevant certifications (AWS Security Specialty, CISSP, CEH, CKA/CKS) are a plus.

PERKS, BENEFITS AND WORK CULTURE:

• Competitive Salary Package
• Generous Leave Policy
• Flexible Working Hours
• Performance-Based Bonuses
• Health Care Benefits

Why this role exists

Our infrastructure footprint is growing faster than our headcount, and we believe most of that

gap should be closed by automation and AI agents — not by hiring more humans to do toil. We

need someone early in their career who treats manual work as a bug, ships scripts and agents

instead of tickets, and wants to grow into deeper ownership over the next two years.

You will not be the most senior person on the team. You will be the one who multiplies the team.

What you'll own

In your first 1 months

• Take ownership of one slice of our CI/CD pipeline and make it measurably

faster, more reliable, or cheaper. We expect a number on a dashboard to move.

• Build at least three internal automations that replace manual ops toil —

using AI agents (Claude Code, agentic CLIs, scripted LLM workflows) as your force

multiplier.

• Be the first responder for a defined set of alerts. Write the runbooks. Drive

the alert volume down.

• Support senior engineers on AI/ML infrastructure (GPU nodes, inference

services, model deployment) — observe, document, and gradually take on contained

changes under review.

By 3 months you should be

• The go-to person for at least two production systems.

• Shipping routine infrastructure changes without needing senior review.

• Treating "manual" as a code smell.

Required (we will reject without these)

• 0–3 years hands-on experience with one major cloud (AWS, GCP, or

Azure — one is fine, depth beats breadth).

• Fluent in Linux command line, bash, and at least one scripting language

(Python or Go preferred).

• Have shipped something to production that real users hit. A side project

counts; a graded coursework lab does not.

• Comfortable with Docker — you can explain what an image vs. a

container is and why it matters.

• Working knowledge of networking fundamentals: DNS, HTTP/HTTPS,

TLS, ports, basic subnets — enough to debug "it works on my machine."

• Git fluency: branches, merges, rebases, conflict resolution.

• CI/CD pipelines — you have authored or substantially modified pipelines

in GitHub Actions, GitLab CI, ArgoCD, Jenkins, or similar. Not just "I clicked Re-run."

• Kubernetes basics — kubectl for real work, can read pod logs,

understand deployments and services, can debug a CrashLoopBackOff without

panicking. You do not need to have run a cluster; you do need to have lived inside one.

• Active user of AI coding agents (Claude Code, Cursor, Copilot, agentic

CLIs, etc.). You should be able to walk us through specific tasks where they made you

faster, and specific tasks where they failed you and how you noticed. "I have tried it" is

not enough.

Bonus (real plus, not required)

• Infrastructure as Code: Terraform, Pulumi, or Ansible.

• Observability: Prometheus/Grafana, Datadog, OpenTelemetry, any APM.

• Have built or extended an LLM-based agent — a custom MCP server, a

scripted multi-step workflow, an internal tool that calls models in a loop. Anything beyond

chat-with-Claude.

• Exposure to GPU workloads, model serving (vLLM, Triton, TGI, etc.), or

ML pipelines.

What we don't care about

• Whether your degree is in CS — or whether you have a degree at all.

• Brand-name companies on your resume.

• Certifications. They are fine. They do not substitute for having shipped.

How we work

• We default to automation. If you do something manually twice, the third

time you script it or hand it to an agent.

• AI agents are part of the workflow, not a novelty. Expect interview

questions about exactly how you use them — and where you have caught them being

wrong.

• Small, reversible changes beat big-bang rollouts.

• Postmortems are blameless and written down.

• We push back on each other. If you only execute, you will be unhappy

here.

How to apply

Send:

• Your resume.

• A short note (≤200 words) describing one infra or automation problem you

solved, and how AI agents factored in — or did not, and why. We read these. Generic

notes get rejected.

Internal note — delete before posting externally

• Comp band, location policy, team name, and reporting line marked

[CONFIRM] need to be filled in before this goes external.

• The Required list is intentionally tight: CI/CD and Kubernetes basics

promoted from bonus. Expect this to filter ~80% of typical junior DevOps applicants. The

remaining pool will skew toward people who have actually shipped infra at a startup, not

bootcamp grads or pure cloud-cert holders.

• IaC, observability, agent-building, and GPU/ML serving stay as bonus.

Promoting any of these to required at 0–3 yrs collapses the pool to near-zero or forces

hiring senior people at junior comp. If you want IaC required, re-level this to mid (3–5

yrs) and raise the band.

• Screening implication: the resume screen should explicitly check for

CI/CD pipeline authorship and any K8s-touching production work. If neither is on the

resume, reject at screen. Do not waste interview slots.

• Pipeline watch: if fewer than ~15 qualified resumes after 2 weeks of

active sourcing, the first thing to relax is the AI-agent-fluency bar (move to bonus and

screen for it in interview instead). Do not relax the "shipped to production" requirement

— that is the load-bearing filter.

Job Title: Senior AIML Engineer – Immediate Joiner (AdTech)

Location: Pune – Onsite

About Us:

We are a cutting-edge technology company at the forefront of digital transformation, building innovative AI and machine learning solutions for the digital advertising industry. Join us in shaping the future of AdTech!

Role Overview:

We are looking for a highly skilled Senior AIML Engineer with AdTech experience to develop intelligent algorithms and predictive models that optimize digital advertising performance. Immediate joiners preferred.

Key Responsibilities:

• Design and implement AIML models for real-time ad optimization, audience targeting, and campaign performance analysis.
• Collaborate with data scientists and engineers to build scalable AI-driven solutions.
• Analyze large volumes of data to extract meaningful insights and improve ad performance.
• Develop and deploy machine learning pipelines for automated decision-making.
• Stay updated on the latest AI/ML trends and technologies to drive continuous innovation.
• Optimize existing models for speed, scalability, and accuracy.
• Work closely with product managers to align AI solutions with business goals.

Requirements:

• Minimum 4-6 years of experience in AIML, with a focus on AdTech (Mandatory).
• Strong programming skills in Python, R, or similar languages.
• Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Expertise in data processing and real-time analytics.
• Strong understanding of digital advertising, programmatic platforms, and ad server technology.
• Excellent problem-solving and analytical skills.
• Immediate joiners preferred.

Preferred Skills:

• Knowledge of big data technologies like Spark, Hadoop, or Kafka.
• Experience with cloud platforms like AWS, GCP, or Azure.
• Familiarity with MLOps practices and tools.

How to Apply:

If you are a passionate AIML engineer with AdTech experience and can join immediately, we want to hear from you. Share your resume and a brief note on your relevant experience.

Join us in building the future of AI-driven digital advertising!

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