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Requirements:
5+ years in Data architecture and Data analytics
Strong hands-on experience with GCP - mandatory (GCS, BigQuery, Pub/Sub etc...)
Experience with AWS - a significant advantage
Strong background in Data Architecture and designing complex data solutions
Proven experience leading projects and/or technical teams
Strong technical leadership and problem-solving skills
Ability to combine architecture, technical leadership, and hands-on execution
Strong communication and collaboration skills.

Requirements:
10+ years of experience as a Database Administrator.
Hands-on experience with PostgreSQL and Microsoft SQL Server (MSSQL).
Strong knowledge of database performance tuning and query optimization.
Experience with backup, recovery, replication, and high availability solutions.
Experience with database security and permission management.
Excellent problem-solving skills and ability to work in a production environment.
Strong communication skills and ability to collaborate across teams.
Nice to Have
Experience with cloud database services (AWS RDS, Azure SQL, or Google Cloud SQL).
Familiarity with monitoring tools such as Grafana, Prometheus, Zabbix, or similar.
Familiarity with scripting languages such as PowerShell, Bash, or Python is an advantage.
Experience with CI/CD pipelines and Infrastructure as Code.
Knowledge of NoSQL databases is an advantage.
Relevant database certifications are a plus.

Requirements:
6-8+ years of hands-on experience in senior software engineering positions.
Hands-on experience building and shipping agentic workflows or LLM-powered systems in production, not just prototypes.
Real experience working across agent harnesses (e.g., Claude Code and similar), and hands-on understanding of agent context: how it's built, scaffolded, and kept effective at scale.
Practical understanding of the actual building blocks: LLMs, RAG, tool use, evaluations, and human-in-the-loop patterns, not just theoretical familiarity.
Strong software engineering fundamentals: you can design, build, and operate a system end to end.
A demonstrated ability to work without a pre-defined spec: given a goal or a KPI, you can find the problem, scope the solution, and build it.
Product judgment: you can separate what's worth building from what isn't, and you know how to tell whether it actually worked.
Strong discovery skills: you can sit with engineers, map how work actually happens today, and identify where automation genuinely helps versus where it doesn't.
Strong communication skills: you can translate between technical tradeoffs and the people who need to understand the impact.
Advantages
A cost-aware mindset: you treat tokens like a resource to be managed, not an afterthought.
Experience building on top of Kubernetes, containers, or CI/CD infrastructure in production.
Experience with MCP-based tooling, agent orchestration frameworks, or LLM gateways.
Familiarity with observability tooling (e.g., Datadog) for monitoring agent or platform health.
Background in a fast-paced, high-growth B2B SaaS environment.
Exposure to health tech, healthcare institutions, or behavioral health.

Requirements:
4+ years of experience leading a team of at least 5 developers, building and maintaining SaaS applications at scale in production
5+ years of software engineering experience working on production applications with real-world users
Experience building E2E Web Services working with big amounts of data.
Proficient in React/Angular/Other modern JS framework and a JavaScript tech stack background
Experience with Python and microservices.
Experience working with AWS.
Working with LLMs - LiteLLM, Langfuse, LangGraph.

Requirements:
At least 5 years of experience with Python in backend or data engineering roles, designing and operating large-scale data pipelines, backend services, and data infrastructure in production environments.
Hands-on experience working on ML/AI-powered products in production, with strong understanding of requirements for integrating data platforms with AI features.
Familiarity with modern LLM (Large Language Model) and RAG (Retrieval-Augmented Generation) technologies, and experience supporting their deployment or integration.
Familiar with or have worked with these technologies (or alternatives):
Data Processing & Streaming: Apache Spark, DBT, Airflow, Airbyte, Kafka
API Development: FastAPI, micro-service architecture, SFTP
Data Storage: Data Lakehouse architectures, Apache Iceberg, Vector Databases, RDS
ML/AI: ML/LLM libraries and frameworks (such as Gemini, Hugging Face, etc.)
Cloud Infrastructure: AWS stack (S3, Firehose, Lambda, Athena, etc.), Kubernetes (K8s)
Demonstrated ability to optimize performance and ensure high availability, scalability, and reliability of backend/data systems.
Strong foundation in best practices for data quality, governance, security, and observability.
Ability to collaborate effectively with engineering, data science, and product teams in a cross-functional setting.
Track record of innovative thinking on feature-level implementations, metric definitions, and AI/data integrations.
Service-oriented approach, particularly in high-responsibility, on-call situations.

Requirements:
7+ years of hands-on experience building, deploying, and operating large-scale, production cloud systems end-to-end.
Strong Computer Science foundations: Solid grasp of software design principles, concurrency, data structures, and cost/performance trade-offs.
Cloud-Native & Event-Driven Systems: Hands-on experience designing and operating cloud-native, event-driven architectures and working with modern databases (SQL, NoSQL, or vector DBs).
Language Proficiency: Deep proficiency in **Python** (or equivalent backend languages).
Cross-Domain Adaptability: Comfortable working across backend contexts, including data pipelines, backend core logic, and applicative microservices.
Ownership Mindset: A strong sense of personal accountability and a proven track record of taking complex backend systems from zero to production.
Education BSc in Computer Science, Software Engineering, or equivalent practical experience.
Advantages :
AI Agent Workflows Experience building or integrating AI agents, LLM orchestration, or modern AI frameworks into production services.
DevOps & CI/CD: Hands-on experience setting up robust, automated CI/CD pipelines and infrastructure-as-code.
Full Stack Flexibility: Prior exposure to modern Frontend/UI frameworks (e.g., React, TypeScript)

Requirements:
3+ years of experience as a Software Developer.
B.Sc. in Computer Science.
Solid computer science fundamentals, including object-oriented design, data structures, algorithms, problem-solving, and complexity analysis.
Hands-on experience in server-side development (e.g., Node.js, TypeScript, Python, or Java).
Frontend development experience in React.js (or similar frameworks such as Angular or Vue.js)
Familiarity with cloud services (AWS, GCP, or Azure) - advantage.
Experience with REST APIs, microservices, Docker, and Kubernetes - advantage.
Fast learner, able to understand new code and technologies quickly.
Highly motivated with a deep desire to achieve outstanding results.
Experience using AI-assisted development tools (e.g., GitHub Copilot, Cursor, ChatGPT, Claude, or similar) to improve software development productivity and code quality.
A team player with a Can Do attitude who goes the extra mile to succeed!