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About This Role
About us
Appnovation is the AI Product Company. Headquartered in Vancouver, Canada, with teams around the world, Appnovation builds AI products, delivers enterprise AI and digital solutions, and co-invests with clients to bring new AI products to market.
We're looking for a Site Reliability Engineer to keep a shared observability platform for LLM-based applications running for a global life sciences client. The platform is built on Langfuse and self-hosted on Kubernetes on AWS, with ClickHouse as the analytical store, PostgreSQL for metadata and Redis as the ingestion queue, all delivered through Argo CD.
Two things need building rather than maintaining. The platform has no monitoring, alerting or defined service levels today, and you will own putting them in place. Infrastructure is defined as code throughout, in Kubernetes manifests, Helm values and Argo CD applications.
Alongside the platform itself, you will own the runbooks that make an incident survivable by someone other than the author, and the onboarding and support path for the internal teams that depend on the platform.
ROLE RESPONSIBILITIES
- Platform Operations: Diagnose and resolve failures across ClickHouse, PostgreSQL, Redis, the ingestion workers and the Kubernetes layer beneath them, including ingest backpressure from queue depth and worker drain behaviour.
- ClickHouse Operations: Own ClickHouse under the operator model, including Keeper quorum, replication, shard and replica topology, and S3 storage tiering.
- Provable Backup and Restore: Rehearse the restore, time it, document it and test it against its failure modes, rather than assuming a successful backup job means a recoverable system.
- Safe Upgrades: Plan and rehearse upgrades in a lower environment, with a rollback plan that works even if a migration is only partly complete.
- Monitoring and Service Levels: Build monitoring, alerting and service levels from scratch, so problems are found here before a user reports them.
- Infrastructure as Code: Maintain Kubernetes manifests, Helm values and Argo CD applications so every change goes through the delivery pipeline.
- Runbooks and SOPs: Write and maintain runbooks and SOPs that let a colleague resolve an incident without the author present.
- Onboarding and Support: Run the onboarding and support path for internal teams that depend on the platform, and triage what they bring.
- Automation: Turn recurring operational work into automation.
- Upgrade Partnership: Work with the platform engineer who owns what the platform offers. They decide what to adopt and how it is configured; you own the migration and its rollback.
QUALIFICATIONS
- Hands-on experience with Kubernetes on AWS (managed EKS), with routine work done through Helm values and Argo CD applications.
- Hands-on experience running ClickHouse in production, including replication and Keeper quorum, shard and replica topology, and backup and restore, ideally run through an operator.
- Experience building monitoring and alerting from scratch, including service levels that reflect what users actually experience rather than what is easy to measure.
- Experience upgrading self-hosted software safely, including schema migrations, rehearsal in a lower environment and a rollback plan for a partly completed migration.
- PostgreSQL and Redis operations deep enough to debug metadata-store and queue problems, including backpressure and worker drain.
- Strong operational writing: runbooks, SOPs and post-incident reviews that a colleague can follow unaided during an incident.
PREFERRED QUALIFICATIONS
- Observability engineering, including OpenTelemetry Collector pipelines, alerting design and Grafana dashboards.
- OIDC or enterprise SSO integration with a corporate identity provider.
- GitHub Actions for plan and apply pipelines with approval gates.
- Experience running LLM observability tools such as Langfuse, LangSmith or Arize Phoenix.
- Experience in pharma, life sciences or another regulated industry.
WHO YOU ARE
- You don't trust a backup until you've restored from it
- You want to find problems before users do
- You write runbooks for the person on call at 3am, not for yourself
- You stay calm in incidents and focus on fixing the process afterward
- You automate anything you have to do twice
- You work well inside a client team and build trust quickly
- You have prior experience in consulting
- Prior experience and connections in the Life Sciences industry is preferred
Thank you for your interest in a career with Appnovation Technologies! Please note that only those selected for an interview will be contacted.
At Appnovation, we recognize that diverse teams are the strongest teams. Diversity, Equity & Inclusion is not only something that we embrace - we celebrate it! We are proud to be an Equal Opportunity Employer and we encourage applicants from all backgrounds, lived experiences and industries to apply. Come join us at Appnovation, and learn more about how we stay true to our company values as we build better lives through better digital.
Accommodations are available upon request throughout the recruitment process.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Appnovation Technologies, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Appnovation Technologies AI Hiring
Appnovation Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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