Senior AI Engineer
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Key skills for this role
About the Role
VentureOne is looking for a Senior AI Engineer to join the Foundry team in Abu Dhabi. You will lead the design and development of AI-enabled products from concept to MVP, including LLM applications, RAG systems, and AI agents.
Key Skills for This Role
Responsibilities
- Lead the design and development of AI enabled products from early concept to MVP, customer validation, and early production readiness
- Translate ambiguous business problems into AI solution designs, experiments, architecture diagrams, evaluation plans, and working systems
- Design and build LLM based applications using commercial and open source models through APIs, hosted inference, or private deployments
- Build RAG systems including document ingestion, parsing, chunking, embeddings, vector search, hybrid search, reranking, citation generation, access control, and answer evaluation
- Build AI agents and tool calling workflows that can use APIs, databases, search, documents, business rules, and human review steps
- Design orchestration patterns for multi step AI workflows, including planning, tool execution, guardrails, retries, fallback paths, state management, and traceability
- Build AI evaluation pipelines using golden datasets, test cases, automated checks, human review, quality scoring, regression testing, and failure analysis
- Evaluate model quality, prompt performance, retrieval accuracy, hallucination rate, latency, cost, reliability, safety, and user experience
- Design AI systems with human oversight, auditability, traceability, permissions, data privacy, and clear failure handling
- Work with structured and unstructured data, including PDFs, images, forms, emails, transcripts, logs, knowledge bases, and enterprise records
- Partner with Senior Full Stack Engineers to integrate AI capabilities into complete products, APIs, dashboards, workflows, and customer facing experiences
- Build reusable AI components such as prompt templates, agent patterns, evaluation harnesses, model gateways, retrieval services, and reference architectures
Requirements
- Strong experience building applied AI products, LLM based applications, AI platforms, or machine learning systems
- Proven ability to lead technical direction for AI systems in complex and ambiguous environments
- Hands on experience with LLMs, prompt design, embeddings, vector databases, RAG, model APIs, structured outputs, and AI orchestration
- Experience building AI agents, tool calling workflows, function calling, workflow automation, or AI powered business processes
- Strong programming experience in Python and strong understanding of production software engineering practices
- Experience with AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, Haystack, AutoGen, CrewAI, or similar
- Experience with vector stores and search systems (pgvector, Pinecone, Weaviate, Qdrant, Elasticsearch, OpenSearch, Azure AI Search)
- Experience designing ingestion pipelines for documents, text, tables, metadata, permissions, and enterprise knowledge sources
- Experience evaluating AI systems using structured test sets, quality metrics, automated evaluation, human review, or production feedback loops
- Good understanding of APIs, back end services, data pipelines, cloud deployment, observability, logging, tracing, and system integration
- Strong understanding of AI safety, responsible AI, privacy, security, prompt injection defense, data leakage prevention, and operational failure modes
- Ability to optimize AI systems for cost, latency, quality, reliability, and scalability
Full Job Posting
About the Role
- Senior AI Engineer to join the VentureOne Foundry team, exploring new venture ideas and building working MVPs.
- Hands on technical leadership role for an AI engineer who can design, build, evaluate, and scale applied AI systems.
- Lead development of AI enabled products including LLM applications, RAG systems, AI agents, workflow automation, copilots, document intelligence, evaluation frameworks, and AI powered decision support tools.
Key Responsibilities
- Lead design and development of AI enabled products from early concept to MVP, customer validation, and early production readiness.
- Translate ambiguous business problems into AI solution designs, experiments, architecture diagrams, evaluation plans, and working systems.
- Design and build LLM based applications using commercial and open source models through APIs, hosted inference, or private deployments.
- Build RAG systems including document ingestion, parsing, chunking, embeddings, vector search, hybrid search, reranking, citation generation, access control, and answer evaluation.
- Build AI agents and tool calling workflows that can use APIs, databases, search, documents, business rules, and human review steps.
- Design orchestration patterns for multi step AI workflows, including planning, tool execution, guardrails, retries, fallback paths, state management, and traceability.
- Build AI evaluation pipelines using golden datasets, test cases, automated checks, human review, quality scoring, regression testing, and failure analysis.
- Evaluate model quality, prompt performance, retrieval accuracy, hallucination rate, latency, cost, reliability, safety, and user experience.
- Design AI systems with human oversight, auditability, traceability, permissions, data privacy, and clear failure handling.
- Work with structured and unstructured data, including PDFs, images, forms, emails, transcripts, logs, knowledge bases, and enterprise records.
- Partner with Senior Full Stack Engineers to integrate AI capabilities into complete products, APIs, dashboards, workflows, and customer facing experiences.
- Build reusable AI components such as prompt templates, agent patterns, evaluation harnesses, model gateways, retrieval services, and reference architectures.
Minimum Qualifications
- Strong experience building applied AI products, LLM based applications, AI platforms, or machine learning systems.
- Proven ability to lead technical direction for AI systems in complex and ambiguous environments.
- Hands on experience with LLMs, prompt design, embeddings, vector databases, RAG, model APIs, structured outputs, and AI orchestration.
- Experience building AI agents, tool calling workflows, function calling, workflow automation, or AI powered business processes.
- Strong programming experience in Python and strong understanding of production software engineering practices.
- Experience with AI frameworks and libraries such as LangChain, LlamaIndex, Semantic Kernel, Haystack, AutoGen, CrewAI, Flowise, or similar tools.
- Experience with vector stores and search systems such as pgvector, Pinecone, Weaviate, Qdrant, Elasticsearch, OpenSearch, Azure AI Search, or similar platforms.
- Experience designing ingestion pipelines for documents, text, tables, metadata, permissions, and enterprise knowledge sources.
- Experience evaluating AI systems using structured test sets, quality metrics, automated evaluation, human review, benchmark based approaches, or production feedback loops.
- Good understanding of APIs, back end services, data pipelines, cloud deployment, observability, logging, tracing, and system integration.
- Strong understanding of AI safety, responsible AI, privacy, security, prompt injection defense, data leakage prevention, and operational failure modes.
- Ability to optimize AI systems for cost, latency, quality, reliability, and scalability.
Preferred Qualifications
- Experience with agentic systems, AI workflow builders, orchestration platforms, multi agent systems, or AI control planes.
- Experience with cloud AI services on AWS, Azure, GCP, or private cloud environments.
- Experience with model gateways, model routing, prompt versioning, prompt registries, experiment tracking, and AI observability.
- Experience with document intelligence, OCR, form extraction, classification, summarization, entity extraction, and knowledge graph enrichment.
- Experience with AI governance, audit trails, human oversight, model evaluation, policy controls, and responsible AI frameworks.
- Experience in regulated domains such as government, fintech, identity, real estate, health, or enterprise platforms.
- Experience building customer facing AI products, not only research prototypes.
- Experience creating reusable AI patterns, reusable prompts, agent templates, evaluation frameworks, and reference architectures.
- Experience deploying AI capabilities with security, tenant isolation, role based access, cost monitoring, and production monitoring.
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