Jobs at AllCloud

Develop and maintain Python backend services and APIs using AWS. Design event-driven workflows with Step Functions, integrate LLMs through AWS Bedrock, and build scalable, highly available systems. Work with microservices, Docker, Kubernetes/EKS, Kafka, databases, observability tools, and RAG frameworks. Collaborate with product, frontend, and data teams to deliver AI-powered solutions, including complex workflows that combine structured logic with LLM reasoning for enterprise and regulated-industry environments.

Own the full AWS services sales lifecycle, including high-velocity prospecting, opportunity development, customer engagement, negotiation, and closing. Build relationships with end-buyers and C-suite decision-makers across virtual and face-to-face environments. Manage complex opportunities for mid-market and startup customers while meeting or exceeding monthly and quarterly sales goals.

Manage front-desk and daily office operations, including supplies, vendors, cleaning, catering, kitchen facilities, maintenance, calls, emails, and visitor reception. Support workplace events, create event materials, improve office processes, and maintain a welcoming, professional environment. The role is part-time and on-site three days per week, requiring strong organization, communication in English and Hebrew, multitasking, and Canva or similar design-tool experience.

Own the full AWS services sales lifecycle, from high-velocity prospecting and opportunity development through negotiation and closing. Engage B2B customers ranging from end buyers to C-suite executives in virtual and face-to-face settings. Manage complex opportunities, exceed monthly and quarterly goals, and build relationships with mid-market and startup clients. This full-time role requires working from the Arlington AWS office two days per week.

Manage end-to-end delivery of complex AWS cloud projects and platform integrations for enterprise clients. Responsibilities include requirements analysis, BRD and SOW creation, stakeholder management, budgeting, resource allocation, risk mitigation, compliance coordination, and project health tracking. The role bridges business needs and technical execution in regulated sectors while collaborating with engineering, architecture, security, and compliance teams.

Supports Salesforce professional and managed services sales by prospecting, developing pipeline, assisting with account growth, preparing business reviews, supporting deal scoping and statements of work, maintaining Salesforce CRM data, coordinating with delivery teams, and developing Salesforce channel relationships. The role is designed for an early-career sales professional working under senior Account Executive mentorship.

Designs and implements scalable Salesforce solutions for clients, translates business requirements into technical architectures, leads project planning and estimation, mentors development teams, integrates Salesforce with other systems, resolves technical issues, and applies Salesforce best practices. Requires strong Salesforce architecture, configuration, security, data migration, data modeling, Agile delivery, analytical, problem-solving, communication, and documentation skills.

Generate and qualify sales leads through cold calls, emails, account research, nurturing campaigns, and appointment setting. Present AWS, Salesforce, and AllCloud solutions to prospective executives, maintain leads and opportunities in Salesforce, and collaborate with marketing and account executives on messaging, account strategy, and early deal stages.

Design and develop scalable Python backend services, distributed systems, microservices, event-driven architectures, APIs, and AI-powered workflows. Integrate LLMs using AWS Bedrock, optimize databases and caching, contribute to cloud infrastructure, CI/CD, and observability, and provide technical leadership through architecture, mentoring, and hands-on development.

Lead the design, deployment, and evolution of enterprise AWS cloud environments. Responsibilities include cloud architecture, CI/CD and infrastructure automation, Kubernetes and container orchestration, observability, monitoring, logging, security architecture, complex troubleshooting, root-cause analysis, customer advisory, technical leadership, code reviews, and mentoring DevOps engineers.

Leads the design, development, deployment, and scaling of generative AI platforms and systems. Responsibilities include building Python-based microservices, applying LLM, RAG, fine-tuning, and inference techniques, designing secure APIs, implementing DevOps practices, collaborating with product and business teams, solving complex technical problems, and mentoring engineers.

Leads the architecture, development, and deployment of enterprise machine learning, Generative AI, NLP, and RAG solutions on AWS. Designs scalable MLOps platforms, data pipelines, feature stores, vector search systems, and model monitoring. Advises enterprise customers, leads technical workshops, collaborates with architects and data scientists, and mentors engineers. Requires advanced Python and ML framework expertise, AWS AI/ML services experience, big data and database knowledge, customer-facing leadership, and fluency in Hebrew and English.

Own end-to-end, high-velocity SMB sales in Israel, selling Salesforce solutions, implementation services, and managed services. Responsibilities include prospecting, discovery, solution scoping, executive and founder engagement, negotiation, closing, pipeline management, quota attainment, and account expansion through upselling and cross-selling. The role requires strong local SMB market knowledge, CRM discipline, persuasive communication, and fluency in Hebrew and English.

Own the full sales cycle for complex enterprise cloud, professional services, and managed services deals in Israel. Build account strategies, advise C-level executives, lead cross-functional teams, negotiate contracts, and drive Salesforce-related transformation revenue. Expand existing accounts through upsell and cross-sell opportunities while managing long-term client relationships and multi-phase deployments.

Lead architecture for an enterprise data warehouse in AWS, designing scalable data infrastructure, pipelines, data lakes, and analytical systems. Select and optimize AWS data services, formats, and processing technologies; establish the technology roadmap; mentor data engineers; and collaborate with developers, data scientists, and end users on BI, AI, and GenAI solutions. Candidates must also pass reliability, security, and polygraph testing.

Design and implement cloud-native data platforms and lakehouse architectures on AWS. Build ETL/ELT pipelines, streaming systems, data services, and AI solutions including RAG and vector search. Apply data modeling, governance, orchestration, observability, and reliability practices. Collaborate with customers and internal teams on technical designs, modernization projects, troubleshooting, code reviews, performance tuning, and production support.

Build, deploy, maintain, and evolve complex AWS cloud environments for customers. Responsibilities include cloud architecture, advanced service deployment, automation, monitoring and logging, troubleshooting, issue resolution, and customer collaboration. The role requires strong Linux, networking, security, containerization, CI/CD, infrastructure-as-code, scripting, Git, and serverless architecture skills, along with fluency in Hebrew and English.

Drive revenue for Salesforce implementation services by building pipelines, managing full sales cycles, creating SOWs, and cultivating C-suite relationships. Collaborate with Salesforce account teams, lead cross-functional internal resources, negotiate large contracts, and achieve quota while supporting strategic enterprise transformations.

Design, develop, and maintain AWS- and Databricks-based data platforms and ETL/ELT pipelines using Python, PySpark, Snowflake, and Delta Lake. Manage Medallion architecture, cloud infrastructure, data warehouses, performance tuning, data modeling, governance, metadata, permissions, lineage, and automated data quality testing. Collaborate with analysts, product managers, and data scientists while troubleshooting distributed systems and database bottlenecks.

Designs, develops, deploys, and optimizes large-scale data pipelines across cloud and on-premise environments. Ingests high-volume data, analyzes datasets with Data Scientists, and builds data solutions for data warehouse and AI projects. The role requires Python, Spark, dbt, pipeline orchestration, container platforms, advanced data formats, AWS data services, and BI tool integration, along with strong analytical, collaborative, and independent working skills.