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Acquism SARL • Doha, Qatar
Role & seniority: Senior AI Engineer; 8+ years experience; contract-based (1 year, extension possible)
Location & work type: Doha, Qatar; onsite/hybrid; start ASAP; visa sponsorship available if needed
Stack / tools: Agentic AI, MCP orchestration; cloud: Azure, GCP; AI platforms: vector stores (Azure AI Search, Pinecone, Weaviate, Milvus), embedding pipelines, RAG; data/streaming: Kafka; CI/CD, OpenShift/Kubernetes, Docker; ML tooling: PyTorch/TensorFlow, MLflow/TFX, LangChain/LlamaIndex; APIs & integration: REST, microservices; data: Spark, Airflow/NiFi; databases: advanced SQL
Design, develop, and deploy Agentic AI solutions (autonomous workflows, multi-agent systems); implement MCP-style orchestration and integrated AI pipelines
Build/maintain AI platform components (vector stores, embeddings, retrieval, document processing, metadata extraction) and integration layers for AI services
Design/deploy scalable, secure cloud-based AI solutions; manage real-time data streaming, model serving (OpenShift/K8s), monitoring, and collaboration with IT and product teams
Deep expertise in AI/ML engineering: LLMs, embeddings, multi-agent/workflow orchestration
Experience with AI orchestration frameworks, MCP or equivalent; vector databases; RAG patterns
Strong software engineering: Python, API development (REST), microservices; advanced SQL; Git
Cloud & data engineering: Azure or GCP; Docker/OpenShift/K8s;
Location: Doha, Qatar
Contract duration: 1 year (extension possible)
Start Date: ASAP
Experience: 8+ years
Salary: TBN
Visa Sponsorship: Available if needed
The Senior AI Engineer is responsible for designing, developing, and deploying Agentic AI solutions and AI-enabled platforms within the Data & AI Lab. The role focuses on building robust AI infrastructure, including integrations with cloud platforms, vector stores, document processing pipelines, and real-time data streaming capabilities.
This position requires strong engineering skills to work with AI orchestration frameworks (MCP), cloud services (Azure, GCP), and modern AI tooling. The AI Engineer collaborates closely with AI Product Owners, Data Scientists, and other engineers to deliver production-grade AI solutions supporting banking operations and customer-facing applications.
The role bridges AI research and production deployment, ensuring AI capabilities are scalable, maintainable, and aligned with enterprise architecture standards.
Key Accountabilities
Key Competencies AI & ML Engineering Strong understanding of LLMs, embedding models, and generative AI architectures. Experience with AI orchestration frameworks, agent development, and multi-step AI workflows. Familiarity with vector databases (Azure AI Search, Pinecone, Weaviate, Milvus) and RAG patterns. Knowledge of deep learning frameworks (TensorFlow, PyTorch) and model deployment tools (MLflow, TFX). Software Engineering Advanced Python and relevant AI/ML libraries (LangChain, LlamaIndex, or similar). Experience with API development (REST) and microservices architecture. Advanced SQL skills (Stored Procedures, Window functions, Temp Tables, Recursive Queries). Git (GitHub/GitLab) for version control and code management. Cloud & Data Engineering Experience with cloud platforms (Azure, GCP) and object storage (S3, GCS, ABS). Knowledge of data streaming technologies (Kafka) and workflow orchestration (Airflow, Apache NiFi). Containerization and orchestration using Docker and Kubernetes (OpenShift). Familiarity with Spark for data processing and MLOps practices. Collaboration Ability to work effectively in cross-functional teams with Data Scientists, ML Engineers, and Product Owners. Strong communication skills to explain technical concepts to non-technical stakeholders. Experience working in Agile/Scrum environments (Kanban, Scrum).
Qualifications & Experience Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field. 6 to 8 years of experience in software engineering, with at least 2 years focused on AI/ML applications. Hands-on experience with cloud platforms (Azure or GCP) and containerization (Docker, OpenShift/K8s). Experience with document processing, metadata extraction, and knowledge management systems preferred. Banking or financial services industry experience is a plus. Relevant certifications (Azure AI Engineer, GCP ML Engineer) preferred.