Senior AI Engineer

Role summary

Agentic AI and platform architecture

• Lead autonomous workflows, multi-agent systems, and enterprise AI orchestration; define agent architecture patterns and MCP integrations.
• Architect vector stores, embedding pipelines, and retrieval systems.
• Design document parsing, processing, and metadata extraction frameworks for enterprise knowledge management.
• Define API standards and integration patterns for AI services across the bank.

Cloud, infrastructure, and MLOps

• Lead Azure and GCP cloud architecture with attention to scalability, security, and cost optimization.
• Design Kafka event-driven architectures for real-time AI and streaming use cases.
• Set standards for model serving on OpenShift/Kubernetes, monitoring, and MLOps.

Technical leadership and governance

• Mentor AI Engineers and lead design reviews, code reviews, and architecture discussions.
• Promote CI/CD, code management, testing, knowledge management, and documentation practices.
• Partner with product owners and business stakeholders to translate requirements into technical solutions.
• Contribute to AI governance and ensure alignment with data policies, ethical standards, and regulatory requirements.
• Define and maintain service-level agreements, monitoring, and alerting for AI solutions.

AI and software engineering competencies

• Expertise in LLMs, embedding models, generative AI, orchestration frameworks, complex agents, and multi-agent systems.
• Expert knowledge of vector databases, RAG, and knowledge retrieval patterns.
• Background in TensorFlow or PyTorch and model deployment tools such as MLflow or TFX.
• Advanced Python and AI/ML frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Strong API design, microservices, event-driven architecture, advanced SQL, Git, and CI/CD practices.

Cloud and data engineering competencies

• Expert-level experience with Azure and/or GCP and object storage such as S3, GCS, or ABS.
• Advanced knowledge of Kafka, Spark, and workflow orchestration such as Airflow or Apache NiFi.
• Expertise in Docker, Kubernetes/OpenShift, and MLOps, including model monitoring, versioning, and management.

Requirements

• Deep expertise in LLMs, embedding models, and generative AI architectures.
• Advanced experience with AI orchestration frameworks, complex agent development, and multi-agent systems.
• Expert knowledge of vector databases, RAG architectures, and knowledge retrieval patterns.
• Strong background in deep learning frameworks such as TensorFlow or PyTorch and model deployment tools such as MLflow or TFX.
• Expert proficiency in advanced Python and AI/ML frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Strong experience with API design, microservices, and event-driven architecture.
• Advanced SQL skills, including stored procedures, window functions, temporary tables, and recursive queries.
• Expert-level experience with Azure and/or GCP and object storage such as S3, GCS, or ABS.
• Advanced knowledge of Kafka, Spark, and workflow orchestration tools such as Airflow or Apache NiFi.
• Expertise in Docker and Kubernetes/OpenShift, plus MLOps practices for model monitoring, versioning, and management.
• Financial services industry experience and ability to align AI solutions with banking regulatory requirements.