We are looking for a highly skilled and visionary Senior Machine Learning Engineer to lead the architecture, design, and deployment of enterprise-grade AI/ML projects on AWS. In this role, you will take full technical ownership of advanced AI initiatives, leveraging both native managed services (such as Amazon SageMaker and Bedrock) and custom-built GenAI models to deliver transformative predictive insights and automation to our customers.
As a Senior ML Engineer, you will bridge the gap between complex data engineering and state-of-the-art data science. You will drive the design of scalable MLOps architectures, optimize high-volume data pipelines, and act as a trusted technical advisor to our clients. You will work closely with solutions architects, project managers, and data scientists, while also mentoring junior and mid-level engineers to elevate the team's technical capabilities.
Responsibilities
Architect and Lead ML Solutions: Spearhead the end-to-end architecture, development, and production deployment of robust Machine Learning models, including advanced predictive analytics, NLP, and Generative AI/RAG systems.
Enterprise MLOps & Automation: Define, design, and implement enterprise-grade MLOps strategies. Establish CI/CD pipelines for ML, automated model training, monitoring, versioning, and governance at scale.
AWS AI/ML Mastery: Architect innovative solutions leveraging AWS AI/ML managed services (e.g., SageMaker, Bedrock) to accelerate time-to-market while ensuring high performance and cost-efficiency.
Advanced Data Engineering: Lead the design of highly scalable infrastructure for extracting, transforming, and loading (ETL) data from diverse sources to support complex ML feature stores and model training.
Unstructured Data & Vector Search: Architect systems for the optimal ingestion, processing, and semantic retrieval of unstructured data (text, images, documents) using Vector Databases (e.g., OpenSearch, Pinecone) and graph-based reasoning.
Strategic Advisory & Collaboration: Act as a trusted AI advisor to external enterprise customers and internal C-level executives. Translate complex business constraints into scalable ML architectures and guide clients through their AI adoption journey.
Technical Leadership & Mentorship: Mentor mid-level and junior engineers, establish coding and architectural best practices, and foster a culture of continuous learning and innovation within the team.
Requirements: Experience: 5+ years of proven, hands-on experience in a Machine Learning Engineer or highly technical Data Scientist role, with a strong track record of deploying scalable ML models to production environments.
Education: Bachelors (Graduate/Masters highly preferred) degree in Computer Science, Mathematics, Information Systems, or a related quantitative field.
Expert Programming & ML Frameworks: Deep expertise in Python and mastery of modern ML/Deep Learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, Hugging Face).
GenAI & LLM Expertise: Strong hands-on experience with Generative AI architectures, including LLMs, fine-tuning methodologies, domain-specific prompting, and RAG pipelines.
Cloud Architecture: Extensive practical experience architecting solutions on AWS, with deep knowledge of AWS AI/ML Services (SageMaker, Bedrock) and core data/compute services (EC2, EMR, Redshift).
Big Data Ecosystem: Proven experience designing complex data pipelines using big data and stream processing technologies (Spark, Kafka, Kinesis, Elasticsearch, Hadoop).
Database Mastery: Advanced SQL proficiency, deep understanding of relational and NoSQL databases (MySQL, Postgres, DynamoDB), and experience with data modeling at scale.
Customer Facing Leadership: Demonstrated ability to lead technical workshops, manage stakeholder expectations, and drive complex projects with external enterprise customers.
Languages: Fluency in Hebrew and English is essential.
This position is open to all candidates.