Senior GenAI Engineer

To ensure you’re set up for success, you will bring the following skillset & experience:

• Bachelor’s degree in Computer Science, Engineering, Data Science, AI/ML, or a related technical field, or equivalent practical experience.
• 6+ years of experience in software engineering, data engineering, machine learning engineering, AI engineering, or related technical roles.
• 2+ years of hands-on experience building LLM, Generative AI, or Agentic AI applications.
• Strong programming experience in Python.
• Experience building production-grade applications, APIs, services, automation workflows, or data-driven solutions.
• Hands-on experience with LLM application development, prompt engineering, RAG architectures, vector search, tool use, and agent orchestration.
• Experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar tools.
• Experience with enterprise AI platforms such as Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar platforms.
• Experience with vector databases or retrieval platforms such as Pinecone, Weaviate, FAISS, Milvus, Azure AI Search, OpenSearch, or similar technologies.
• Strong understanding of hallucination mitigation, grounding techniques, prompt injection risks, AI safety, guardrails, evaluation, and monitoring.
• Experience integrating AI solutions with APIs, databases, data warehouses, enterprise systems, and business applications.
• Ability to translate business problems into practical AI solution designs.
• Strong communication skills and ability to work effectively with both technical and non-technical stakeholders.
• Hands-on builder mindset with the ability to move beyond demos and build enterprise-grade AI solutions that are grounded, tested, monitored, secure, and scalable.

Whilst these are nice to have, our team can help you develop in the following skills:

• Experience building AI solutions for Sales, Revenue Operations, Customer Success, Marketing, Partner, or other GTM teams.
• Experience with CRM platforms such as Salesforce.
• Experience with cloud platforms such as Azure, AWS, or Google Cloud.
• Experience with data platforms such as Snowflake, Databricks, BigQuery, or similar technologies.
• Experience with AI observability, logging, tracing, model evaluation, feedback collection, and performance monitoring.
• Experience with prompt lifecycle management, including prompt versioning, testing, approval workflows, regression testing, and performance tracking.
• Experience with CI/CD, Git, DevOps, containers, secure software development, and production deployment practices.
• Familiarity with enterprise security, role-based access control, data privacy, governance, compliance, and responsible AI standards.
• Exposure to designing reusable AI engineering patterns that can scale across multiple enterprise use cases.