Machine Learning Engineer at Voyantis

Tel Aviv, Tel-Aviv District, Israel -

Full Time

Start Date

Immediate

Expiry Date

11 Jan, 26

Salary

0.0

Posted On

13 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, AI Infrastructure, Python, Data Pipelines, MLOps, SQL, Serverless Architecture, CI/CD, Monitoring Systems, Data Transformation, Software Engineering, Real-Time Data Streaming, Kafka, Flink, UI Development, React, Plotly Dash

Industry

technology;Information and Internet

Description

Description We are looking for an excellent ML Engineer to join our growing team! In this role, you will be responsible for building and scaling our end-to-end ML and AI infrastructure. You will design and implement the inference services that support both streaming and batch processing, develop the agentic infrastructure for our internal AI agents, and optimize the full MLOps lifecycle from training to production monitoring. Working with cutting-edge serverless technologies, you'll ensure our systems perform efficiently and reliably, directly impacting our product's success. You'll be joining a team that bridges the gap between data science innovation and production excellence. About Us: Voyantis is the pioneer in the emerging space of enabling growth teams to acquire, engage, and retain their most valuable customers, based on fusing per-customer LTV predictions with prescriptive AI. Voyantis was founded in 2020 on the premise that market fundamentals are shifting companies worldwide from growth-at-all-costs strategies to efficient and responsible growth practices, with a focus on improving Unit Economics. With a bold mission to leverage AI to reimagine the whole Growth process, to streamline this transition and ensure its sustainability, Voyantis eliminates the guesswork from customer value creation, empowering leaders with actionable strategies and tactics to acquire, nurture and retain the high-value customers their businesses really need, with the actions and the timing that would be most impactful to achieve their goals. Leading companies like Miro, Rappi and Moneylion rely on Voyantis to effectively apply these predictions. They use Voyantis to drive high-value customer acquisition on platforms like Google and Meta, optimize incentives through Salesforce and Braze, and perfectly time upsells, resulting in a 20%-40% ROI uplift. Responsibilities Design and implement scalable ML/AI infrastructure and pipelines using serverless architecture Collaborate with data scientists and ML engineers to optimize model lifecycle, data pipelines and improve operational efficiency Develop and maintain monitoring systems for ML models in production, ensuring reliability, optimizing performance, and troubleshooting issues as needed. Develop and maintain data transformation pipelines using modern tools like dbt and Snowflake Implement and maintain CI/CD pipelines for ML models and supporting infrastructure Drive best practices in software engineering and MLOps across the organization Requirements 3+ years of hands-on experience designing and developing scalable backend infrastructure, with strong proficiency in Python. Demonstrated ability to work effectively with a range of data files (Parquet, JSON, etc.) and perform complex operations using dataframes. Experience with cloud services, particularly serverless architecture Adept at writing SQL queries, with a working knowledge of query optimization, data flow patterns, and pipeline construction. Good understanding of software engineering best practices, including version control, testing, and CI/CD Nice to Haves: Experience with real-time data streaming and processing using technologies like Kafka and Flink. Familiarity with AI agent frameworks and infrastructure, such as LangChain, DSPy, and building feedback loops for autonomous systems. Experience with UI development for data applications using tools like React or Plotly Dash.

Responsibilities

Design and implement scalable ML/AI infrastructure and pipelines using serverless architecture. Collaborate with data scientists and ML engineers to optimize model lifecycle and improve operational efficiency.