Senior AI Engineer

Job summary

Agentic AI and platform leadership

• Lead development of autonomous workflows, multi-agent systems, and enterprise AI orchestration.
• Define architecture patterns and best practices for AI agent development and MCP integrations.
• Evaluate emerging technologies and recommend adoption strategies.
• Architect vector stores, embedding pipelines, and retrieval systems.
• Design document parsing, processing, and metadata extraction for enterprise knowledge management.
• Define API standards and integration patterns for AI services across the bank.

Cloud, infrastructure, and technical leadership

• Lead AI cloud architecture on Azure and GCP, 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.
• Mentor AI engineers; lead technical design reviews, code reviews, and architecture discussions.
• Promote CI/CD, code management, testing, knowledge management, and documentation practices.
• Coordinate with enterprise architecture and IT teams to align with bank technology strategy and target data architecture.

Stakeholder engagement and governance

• Partner with AI product owners and business stakeholders to translate requirements into technical solutions.
• Explain technical concepts and trade-offs to senior management and steering committees.
• Contribute to AI governance and ensure alignment with data policies, ethical standards, and regulatory requirements.
• Define and maintain AI solution SLAs and implement monitoring and alerting.

AI and machine learning competencies

• Deep expertise in LLMs, embedding models, and generative AI architectures.
• Advanced experience with orchestration frameworks, complex agent development, and multi-agent systems.
• Expert knowledge of vector databases, RAG architectures, and knowledge retrieval patterns.
• Strong background in TensorFlow or PyTorch and model deployment tools such as MLflow or TFX.
• Experience with end-to-end ML lifecycle management and model monitoring.

Software engineering competencies

• Advanced Python proficiency and AI/ML frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Experience with API design, microservices, and event-driven architecture.
• Advanced SQL, including stored procedures, window functions, temporary tables, and recursive queries.
• GitHub or GitLab, CI/CD pipelines, and code management 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 and Kubernetes/OpenShift.
• MLOps practices, including model monitoring, versioning, and management.

Experience and source completeness

• The job summary calls for financial-services industry experience.
• The source description ends during the Leadership & Communication competencies section; remaining details are unavailable.