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Requirements:
● 4+ years of experience in Customer Success, Account Management, or Consulting
● Experience working with or selling into eCommerce or retail technology clients
● Strong understanding of SaaS, AI, or data-driven products
● Proven track record of leading successful POCs and pilots in complex B2B Enterprise
environments
● Exceptional communication and stakeholder management skills
● Entrepreneurial mindset with the ability to build from scratch in a fast-paced startup
● Great presentation skills, persuasion and creativity.
● Analytical and metrics-driven approach to problem-solving
● Experience working in a startup or early-stage tech company.

Requirements:
8+ years of experience in software development, system design, and architecture roles.
Strong hands-on experience with modern backend technologies such as Java and/or Python.
Practical experience with containerized and cloud-native systems (Docker, Kubernetes, AWS / Azure / GCP).
Experience designing and operating distributed systems, including messaging or streaming platforms (e.g., Kafka, RabbitMQ, Spark).
Solid understanding of distributed databases (and non-distributed) such as Postgres, Cassandra, SingleStore, Clikhouse or similar technologies.
Deep knowledge of engineering best practices: CI/CD, DevOps, testing, security, and agile development.
Proven experience working with production environments, including deployment pipelines, monitoring, and troubleshooting.
Strong problem-solving skills and the ability to balance ideal architecture with real-world constraints.
Natural technical leadership: able to influence, mentor, and collaborate without relying on authority.
Excellent communication skills and the ability to work effectively across teams.
B.Sc. or M.Sc. in Computer Science, Software Engineering, or equivalent practical experience.

Requirements:
⁠Have 6+ years of software engineering experience, and experience managing a team.
A mix of Management with Hands-on role: lead and mentor in-house + offshore engineers, keep coding/reviewing, and set the bar on architecture, reliability, and delivery.
Worked in cloud-based Big Data environments, Orchestrated and designed data pipelines, and has some experience in holding a robust Machine Learning (AI) stack, including MLOps.
Own production end-to-end: CI/CD, observability, on-call, incident response/postmortems - while balancing fast features with long-term technical health and cost.
Partner closely with the CTO and Product on roadmap, integrations, and security; high autonomy and impact.
What helps:
Strong backend (Python/Scala), AWS, and ML-in-prod (registries/monitoring/rollback); track record of high-scale systems; data platforms/personalization a plus.
Basic know how in FE (TS/React)
Preferred Skills
Experience in, Data Platforms, Personalization, Marketing Intelligence Solutions or decisioning systems.
Background in ML model productionization (registries, monitoring, rollback).
Familiarity with privacy-by-design and compliance-driven delivery.

Requirements:
What you'll need:
10+ years of experience building backend systems in large-scale production environments, with at least 2 years as a Staff Engineer or equivalent level.
Strong programming skills in Python, Java, Kotlin, or Node.js.
Hands-on experience with cloud-native technologies (AWS, Kubernetes, Docker).
Proven ability to design and maintain high-scale distributed systems
Strong sense of ownership, autonomy, and accountability.
Excellent communication skills, with the ability to explain complex systems clearly to both technical and non-technical audiences - including direct collaboration with customers worldwide.

It'd be cool if you also have:
Experience with API Gateway architectures, schema/versioning strategies, or platformization efforts.
Familiarity with real-time data processing frameworks (e.g., Flink, Storm) and resilience patterns.
Background working alongside data science or machine learning teams.
Contributions to developer platforms, infrastructure services, or internal tools improving engineering velocity.

Requirements:
At least 5 years of hands-on experience in software QA and test automation, including 2+ years leading engineers - Must.
Strong programming skills in Python (or equivalent) for building automation frameworks - Must.
BSc. in Computer Science, Computer Engineering, or a relevant scientific field (advanced degrees are an advantage) - Must.
Proven experience designing and maintaining automated test suites and frameworks for high-level software (SDKs, APIs, services) - Must.
Experience handling escalated technical support or working directly with customers on complex software issues - Advantage.
Experience working in a multidisciplinary environment - Advantage.
Experience testing complex systems with demanding performance or reliability requirements - Advantage.
A motivated and resourceful problem solver with a passion for tackling complex technical challenges and raising the quality bar across teams.

Requirements:
What You Need for the Role
6+ years building and shipping production backend systems, including experience leading technical initiatives, processes and teams.
5+ years experience managing engineers, hiring, running 1:1s, giving feedback, and developing people, not just tech-leading.
Strong server-side engineering in at least two of: Node.js / JavaScript, Python, Java. Able to own a feature from data model to API.
Solid PostgreSQL - schema design, query writing and optimization, working with relational data at scale.
Proven experience designing and operating APIs (HTTP/REST/GraphQL) and a working command of auth and access control: OAuth 2.0, API keys, scopes, and RBAC.
Hands-on experience building LLM-powered features in production - direct work with LLM provider APIs (Anthropic, OpenAI, Gemini, Ollama or similar), function/tool calling, token optimization, structured outputs, and prompt engineering.
Experience with agentic systems: designing tool interfaces an LLM can use reliably, multi-step agent loops, and handling failure and recovery.
Working knowledge of MCP (Model Context Protocol) - or the depth in tool-calling and AI integration to ramp on it quickly - and a clear sense of what makes a tool definition easy for a model to use well.
Experience evaluating LLM systems: building eval suites, measuring tool-call and decision accuracy, and instrumenting AI behavior with tracing and observability.
A security mindset -treat prompt injection, untrusted inputs, least-privilege design, and data exposure as first-order concerns, not afterthoughts.
Comfortable in a small, fast-moving startup - high ownership, low hand-holding, and a willingness to build processes and infrastructure from scratch when needed.


Advantages
Retrieval and context engineering - RAG, embeddings, vector search, and semantic retrieval used to ground agents in API docs, schemas, and internal knowledge.
Experience running an MCP server, or a comparable AI-tooling integration, in production.
Cloud infrastructure (AWS / GCP / Azure), containers, and IaC.
Cybersecurity domain knowledge.
Open-source contributions to AI / agent tooling.

Requirements:
5+ years of experience working with Python/C++ - Must.
Experience using Docker or similar tools - Must.
Experience with gRPC -Advantage.
Experience working in a multidisciplinary environment - Advantage.
Experience with CI/CD systems and automated testing frameworks - Advantage.
Proficiency in Linux/Unix environments - Advantage.
A highly motivated problem solver with a passion for tackling complex technical challenges, especially in hardware-oriented environments.

Requirements:
Who are you?
7+ years of software engineering experience, with a strong track record of shipping production systems.
Demonstrated experience working directly with non-technical teams to deliver technology-driven transformation.
Exceptional communication skills: you can run a discovery session with a VP of Sales, write a clear project brief, present results to an executive audience, and pair with a junior analyst on prompt engineering, all in the same week.
Strong product instincts and critical thinking. You naturally think in terms of user problems, adoption, and measurable outcomes rather than technical elegance for its own sake.
Comfortable with ambiguity and self-direction. You won't have a detailed backlog handed to you; you'll build it by understanding the business.
Passionate about LLMs, prompt engineering, and AI application patterns (agentic and autonomous workflows, RAG, agents, tool use).
Solid understanding of GenAI, LLMs and foundation models.
Solid familiarity with AI coding tools like Claude Code, Github Copilot, Cursor, or similar.
Hands-on experience building internal/external AI-driven workflows, agentic frameworks, evals, RAGs, MCPs, skills, etc.
Fluent in written and spoken English.

Itd be really cool if you also:
Have led or played a central role in an AI or digital transformation initiative, with measurable results you can speak to.
Have experience in product management, solutions engineering, or technical consulting roles in addition to software engineering.
Are familiar with GTM/G&A tooling ecosystems (CRM, BI platforms, marketing automation) and understand how AI can augment them.
Have experience designing and delivering technical training or enablement programs.

Requirements:
3+ years in technical customer success, solutions, or onboarding for a data or SaaS product.
Comfort with data concepts, integrations, and APIs, enough to go deep with technical buyers.
Excellent communication; you make complex systems feel simple.
Ownership mentality and genuine customer empathy.

Requirements:
3- 5 years of experience with UVM, System Verilog.
Ability to ramp up verification environments from scratch.
Knowledge of Verification IPs and protocols (PCIe, DDR, AXI).
Good understanding of HW/SW interaction- Advantage
Knowledge in C/C++/Python/System C - Advantage

Requirements:
At least 5 years experience.
Ability to ramp up verification environments from scratch
Experience with UVM, System Verilog - Advantage
Knowledge of Verification IPs and protocols (PCIe, DDR, AXI)
Good understanding of HW/SW interaction- Advantage
Knowledge in C/C++/Python/System C - Advantage

Requirements:
What you'll need:
7+ years of production backend or data infrastructure experience.
Excellent teamwork and communication skills, with a demonstrated ability to explain complex systems and facilitate collaborative solutions.
Deep experience with stream processing systems (Flink, Kafka, or equivalents) at production scale, including failure modes, recovery, and operational burden.
Strong distributed systems instincts: you think about correctness and reliability before you think about features.
Experience with data quality or observability infrastructure. You've built or owned the systems that keep a data platform honest.
Internal product mindset: you treat the teams you serve as customers and make deliberate choices about what to build vs. what not to build.

It'd be really cool if you also:
Have worked with data contracts, schema registries, or data governance tooling.
Have experience with JVM languages (Scala, Java, Kotlin) and big data DBs (Aerospike/elasticsearch/etc).
Have contributed to agentic or LLM-adjacent infrastructure. You understand what "data quality for agents" actually means in practice.
Have operated at a team technical anchor level in a small, high-scope team where breadth of ownership is the norm.

Requirements:
0 to 1 year in sales, customer outreach, retail, support, or communications.
Native-level English, spoken and written.
Strong interpersonal skills for professional cold outreach.
Self-motivated and coachable.
Interest in SaaS, data tools, or analytics.
Basic familiarity with CRM tools (e.g. HubSpot or Salesforce), or a willingness to learn.
Solid organizational and time-management skills.