Machine Learning Solutions Architect
Location
United States
Posted
72 days ago
Salary
0
Seniority
Senior
Job Description
Machine Learning Solutions Architect
phData
• Designing and implementing data solutions best suited to deliver on our customer needs — from model inference, retraining, monitoring, and beyond — across an evolving technical stack. • Providing thought leadership by recommending the technologies and solution design for a given use case, from the application layer to infrastructure; and they have the team leadership and coding skills (e.g. Python, Java, and Scala) to build and operate in production; and to help ensure performance, security, scalability, and robust data integration. • Design and create environments for data scientists to build models and manipulate data • Work within customer systems to extract data and place it within an analytical environment • Learn and understand customer technology environments and systems • Define the deployment approach and infrastructure for models and be responsible for ensuring that businesses can use the models we develop • Demonstrate the business value of data by working with data scientists to manipulate and transform data into actionable insights • Reveal the true value of data by working with data scientists to manipulate and transform data into appropriate formats in order to deploy actionable machine learning models • Partner with data scientists to ensure solution deployability—at scale, in harmony with existing business systems and pipelines, and such that the solution can be maintained throughout its life cycle • Create operational testing strategies, validate and test the model in QA, and implementation, testing, and deployment • Ensure the quality of the delivered product
Job Requirements
- At least 6 years experience as a Machine Learning Engineer, Software Engineer, or Data Engineer
- 4-year Bachelor's degree in Computer Science or a related field
- Experience deploying machine learning models in a production setting
- Expertise in Python, Scala, Java, or another modern programming language
- The ability to build and operate robust data pipelines using a variety of data sources, programming languages, and toolsets
- Strong working knowledge of SQL and the ability to write, debug, and optimize distributed SQL queries
- Hands-on experience in one or more big data ecosystem products/languages such as Spark, Snowflake, Databricks, etc.
- Familiarity with multiple data sources (e.g. JMS, Kafka, RDBMS, DWH, MySQL, Oracle, SAP)
- Systems-level knowledge in network/cloud architecture, operating systems (e.g., Linux), and storage systems (e.g., AWS, Databricks, Cloudera)
- Production experience in core data technologies (e.g. Spark, HDFS, Snowflake, Databricks, Redshift, & Amazon EMR)
- Development of APIs and web server applications (e.g. Flask, Django, Spring)
- Complete software development lifecycle experience, including design, documentation, implementation, testing, and deployment
- Excellent communication and presentation skills; previous experience working with internal or external customers.
Benefits
- Remote-First Work Environment
- Casual, award-winning small-business work environment
- Collaborative culture that prizes autonomy, creativity, and transparency
- Competitive comp, excellent benefits, 4 weeks PTO plus 10 Holidays (and other cool perks)
- Accelerated learning and professional development through advanced training and certifications
Related Guides
Related Categories
Related Job Pages
More Solutions Engineer Jobs
Solutions Engineer
AlpacaDBAlpacaDB, Inc., also known as Alpaca and Alpaca Securities, is an API stock and crypto brokerage platform that enables services to embed investing and developer
• The Solutions Engineer will operate as the technical expert, interfacing with our enterprise customers around the world to help scope and support product integrations from conception to launch, in the following ways • Sales process: Driving product discovery to help formulate a product MVP, as well as providing product demonstrations. • Onboarding Integration: Supporting the integration project as the primary technical resource • Post Integration: Providing any additional solutioning a partner needs, completed, such as implementing a new feature • Internally, the Solutions Engineer will be a key partner to many teams, which include but are not limited to Product, Engineering, Brokerage Operations, Sales, and Customer Success. Simply put, the technology consultant is the voice of the customer internally at Alpaca. • As a technical resource in a highly collaborative role, the ideal candidate will demonstrate a strong solution orientation, as well as being an excellent communicator, troubleshooter, project manager, and team player.
• prowadzenie rozmów z klientami i zamienianie ich potrzeb na projekty z zakresu Big/Cloud Data • doradztwo przy wyborze technologii i architektur, z uwzględnieniem kosztów, ryzyk i potencjalnych korzyści • projektowanie rozwiązań Big/Cloud Data wspólnie z zespołami inżynierskimi • wspieranie sprzedaży poprzez wiedzę ekspercką • analiza organizacji klientów i identyfikowanie zagrożeń i szans (technicznych i biznesowych) • współtworzenie strategii i wizji rozwoju linii Data Solutions w naszej firmie • tworzenie i dbanie o społeczność inżynierów w obszarze data, stymulowanie wymiany wiedzy, zarówno wewnątrz linii biznesowej, jak i całej organizacji • budowanie wizerunku eksperckości linii biznesowej Data Solutions w branży data poprzez udzielanie się na forum poza firmą (np. występowanie na konferencjach branżowy).
• opracowuje architekturę systemów • wpływa na rozwój standardów wytwarzania oprogramowania w całej firmie • pracuje z klientem (w grę wchodzą delegacje) • pracuje z zespołem developerskim • doradza innym i wymienia się wiedzą • angażuje się w działania sprzedażowe — wspiera zespół Presales i Account Managerów delivery • wytwarza oprogramowanie przy pomocy narzędzi i technik Generative AI • wspiera adopcję narzędzi AI w zespole i procesie • uwzględnia możliwości AI przy projektowaniu architektury i podejmowaniu decyzji technologicznych.
• opracowuje architekturę systemów • wpływa na rozwój standardów wytwarzania oprogramowania w całej firmie • pracuje z klientem (w grę wchodzą delegacje) • pracuje z zespołem developerskim • doradza innym i wymienia się wiedzą • angażuje się w działania sprzedażowe — wspiera zespół Presales i Account Managerów delivery • wytwarza oprogramowanie przy pomocy narzędzi i technik Generative AI • wspiera adopcję narzędzi AI w zespole i procesie • uwzględnia możliwości AI przy projektowaniu architektury i podejmowaniu decyzji technologicznych.
