Solutions Architect

  • Sydney, Australia
  • Full Time
  • Customer Success
  • Experienced is the leading AI cloud company, on a mission to democratize AI for everyone. Customers use the H2O AI Hybrid Cloud platform to rapidly solve complex business problems and accelerate the discovery of new ideas. is the trusted AI provider to more than 20,000 global organizations, including AT&T, Allergan, Bon Secours Mercy Health, Capital One, Commonwealth Bank of Australia, GlaxoSmithKline, Hitachi, Kaiser Permanente, Procter & Gamble, PayPal, PwC, Reckitt, Unilever and Walgreens, over half of the Fortune 500 and one million data scientists. Goldman Sachs, NVIDIA and Wells Fargo are not only customers and partners, but strategic investors in the company.’s customers have honored the company with a Net Promoter Score (NPS) of 78 — the highest in the industry based on breadth of technology and deep employee expertise.

The world’s top 20 Kaggle Grandmasters (the community of best-in-the-world machine learning practitioners and data scientists) are employees of A strong AI for Good ethos to make the world a better place and responsible AI drive the company’s purpose.

Please join our movement at

As a Solutions Architect on our Professional Services team you will work closely with technical teams on the customer side and Customer Success, Enterprise Support, Product Engineering and Sales teams on the H2O side.

What You Will Do

  • Be the trusted solutions advisor for our customers and partners and deliver technical professional services to the customer.
  • Translate business cases and requirements into value based technical solutions through the architecture of machine learning workflows and systems from data ingestion to model deployment.
  • Architect, design, and deliver end to end Machine Learning and Data Science solutions that help customers realise the value of their investments in products and services.
  • Communicate effectively with a diverse audience of internal and external stakeholders consisting of: Engineers, business people, partners, executives.
  • Working closely with H2O Data Scientists in advising and developing end to end machine learning solutions (from a data engineering perspective) for the customer requirements.
  • Be involved in providing services that include installation, configuration and integration of H2O Machine Learning, Model Management and Cloud products in the customer environment/landscape.
  • Additionally you would be responsible for helping the customer to integrate the machine learning models/pipelines (python and mojo scoring pipelines) with customer systems
  • Provide/gather customer feedback so that you can work with the Engineering team to further enhance our products.
  • Present at meetups and webinars in the Data Science community, and be an integral part of the Maker culture of creating the best products and solutions.

What We Are Looking For

  • Bachelor’s or a higher education degree in Computer Science/Engineering
  • Minimum 6 to 8 years of experience with cloud computing/linux systems and architecting end to end data processing pipelines or data intensive enterprise solutions

System/Cloud Architecture Skills

  • Excellent understanding of system engineering concepts and good command of working in a linux based environment (OS fundamentals etc). Ubuntu and CentoOS/RHEL mainly.
  • Excellent understanding  and implementation experience of the cloud ecosystem i.e. what solutions exist on the 3 major clouds (AWS, GCP, Azure) and when/how to use those components
  • Excellent understanding and implementation experience with containers and container based orchestration frameworks and leveraging them in the development/solutioning process. Kubernetes and Docker knowledge must have. Virtualization knowledge (vagrant etc.) is a plus.
  • Excellent understanding of distributed systems/services architecture and enterprise grade solutions/software
  • Good understanding and implementation/integration experience of LDAP, OAuth, OpenID, SSH, TLS, network connectivity, firewalls etc that are required in any enterprise grade service architecture/solution

Data Engineering Skills

  • Excellent understanding and experience at building data pipelines and processes supporting data transformation, data structures, metadata, dependency and workload management in ‘big data’ environments.
  • Experience of manipulating, processing and extracting value from large disconnected datasets.
  • Excellent understanding and experience with big data tools like Hadoop and Spark/Kafka for batch and streaming processing of data.
  • Excellent understanding and experience of SQL query language and working with relational databases.
  • Experience of NoSQL database types and understanding of their disparate application scenarios

Programming Languages/Frameworks

  • Experience in Python, Java, Bash scripting is a must have. Go, R, Groovy, Scala are a plus
  • Experience in cloud IaaC sdks like CloudFormation/DeploymentManager or Terraform
  • Experience in configuration management tools like Ansible, chef etc.
  • Experience with writing REST API using micro services frameworks in Python or Java

Data Science skills

  • Experience with Data Engineering pipelines
  • Knowledge of basic statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.)

How to Stand Out From the Crowd

  • Experience of working in a customer facing environment, providing technical services
  • Excellent communication skills  (verbal and written, English language). Additional languages a plus.
  • Amicable attitude. Aptitude to independently investigate and find solutions to technical problems; urge to learn/master new technologies. Maker mindset.


  • Competitive compensation
  • Pre-IPO equity
  • Flexible working environment
  • Be part of a world-class team
  • Career Growth is committed to creating a diverse and inclusive culture.  All qualified applicants will receive consideration for employment without regard to their race, ethnicity, religion, gender, sexual orientation, age, disability status or any other legally protected basis.

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