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About This Role
About Bitdeer Technologies Group
Bitdeer is a world\-leading technology company for AI and Bitcoin mining infrastructure.
Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers and building AI computational infrastructure to support the AI revolution. Bitdeer handles complex processes involved in computing such as equipment procurement, transport logistics, data center design and construction, equipment management, and daily operations. Bitdeer also offers advanced cloud capabilities to customers with high demand for artificial intelligence.
Headquartered in Singapore, Bitdeer has deployed data centers across multiple countries, including the United States, Norway, Bhutan, and Ethiopia.
To learn more, visit https://ir.bitdeer.com/
Position Summary:
We are seeking a highly skilled and motivated Cloud Senior DevOps Engineer to join our AI Cloud team. In this high\-impact role, you will be the backbone of our deployment and infrastructure operations, ensuring that our AI\-powered products and platforms are delivered with speed, security, and exceptional reliability. You will act as a crucial bridge between our research/development teams and real\-world deployment, driving automation, optimizing cloud\-native architectures, and establishing best practices for MLOps and traditional DevOps workflows.
Key Responsibilities:
- CI/CD \& MLOps Pipeline Management: Design, implement, and maintain end\-to\-end CI/CD pipelines for both software applications and machine learning models. Automate build, test, deployment, and rollback processes to ensure seamless transitions from innovation to production.
- Cloud\-Native \& AI Infrastructure: Build, optimize, and scale cloud\-native infrastructure using Kubernetes (K8s) and Docker. Manage and provision specialized computing resources (e.g., GPU clusters) to support high\-performance AI workloads and model inferencing.
- High Availability Architecture: Take ownership of high\-availability design in production environments. Implement disaster recovery (DR) strategies, self\-healing mechanisms, capacity planning, and performance tuning to meet stringent business SLAs.
- Infrastructure as Code (IaC): Champion IaC practices utilizing tools such as Terraform, Ansible, and Helm to achieve fully automated, reproducible, and auditable infrastructure provisioning across multiple cloud environments.
- Observability \& Monitoring: Architect and refine comprehensive monitoring, logging, and alerting systems (e.g., Prometheus, Grafana, ELK/EFK stack) to provide deep visibility into system health, application performance, and AI model metrics.
- Cross\-functional Collaboration: Work closely with R\&D, Data Science, Security, and Business teams to streamline workflows, eliminate bottlenecks, and continuously elevate engineering efficiency through Internal Developer Platforms (IDP) and Platform Engineering initiatives.
- Governance, Security \& Compliance: Establish and enforce robust system stability and security standards. Manage release workflows, implement Zero Trust access controls, oversee secrets management, and ensure compliance with industry frameworks (e.g., SOC2, ISO27001\).
- Incident Management \& Resolution: Act as the technical lead during complex system anomalies and major incidents. Spearhead rapid troubleshooting, conduct thorough root cause analysis (RCA), and implement preventative remediation plans.
Basic Qualifications:
- Experience \& Education: Bachelor's degree or above in Computer Science, Engineering, or a related technical field, with 5\+ years of hands\-on experience in DevOps, Site Reliability Engineering (SRE), or Cloud Infrastructure roles.
- Networking \& OS: Expert\-level knowledge of Linux operating systems and core networking principles (TCP/IP, DNS, HTTP, Load Balancing, VPCs).
- Containerization \& Orchestration: Deep mastery of Docker and Kubernetes orchestration, including a thorough understanding of underlying principles, cluster management, and production\-level best practices.
- Cloud Platforms: Proven proficiency in designing and managing infrastructure on major Public or Hybrid Cloud platforms (e.g., AWS, GCP, Azure, Alibaba Cloud), including multi\-cloud and hybrid\-cloud strategies.
- Programming Skills: Strong coding and scripting capabilities in at least one major language (Go, Python, Shell, etc.) with a solid engineering\-oriented mindset focused on automation and tooling development.
- Domain Knowledge: Systematic and practical understanding of CI/CD methodologies, Infrastructure as Code (IaC), Observability paradigms, and Site Reliability Engineering (SRE) principles.
- Soft Skills: Exceptional problem\-solving abilities, sharp technical judgment, and excellent cross\-team communication skills to effectively collaborate in a fast\-paced, dynamic environment.
Preferred Qualifications (Plus):
- AI/ML Infrastructure Experience: Familiarity with MLOps practices, model serving/inferencing frameworks (e.g., vLLM, TGI, Triton Inference Server), and experience managing GPU clusters for AI/ML workloads.
- Large\-Scale Systems: Proven track record working with large\-scale distributed systems or high\-concurrency environments (e.g., Fintech, Trading, Real\-time processing, or AI platforms).
- Platform Engineering: Hands\-on experience in designing and building Internal Developer Platforms (IDP) to enhance developer autonomy and productivity.
- Advanced Security: Deep familiarity with Zero Trust architecture, automated security testing (DevSecOps), and implementing strict compliance frameworks (e.g., SOC2, ISO27001\).
- Leadership: Prior experience acting as a Technical Lead, mentoring junior engineers, or managing DevOps teams.
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*Bitdeer is committed to providing equal employment opportunities in accordance with country, state, and local laws. Bitdeer does not discriminate against employees or applicants based on conditions such as race, color, gender identity and/or expression, sexual orientation, marital and/or parental status, religion, political opinion, nationality, ethnic background or social origin, social status, disability, age, indigenous status, and union.*
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bitdeer Technologies Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bitdeer Technologies Group AI Hiring
Bitdeer Technologies Group has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Jose, CA, US, Austin, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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