Assistant Vice President _Applied AI Engineer

Role Overview : Assistant Vice President Applied AI Engineer to design and build enterprise-scale AI/ML

solutions. The role focuses on Generative AI, Agentic AI, LLM applications, AI evaluation, MLOps, and scalable

AI infrastructure. The successful candidate will bridge AI architecture and hands-on engineering, delivering

secure and production-ready solutions while mentoring engineers.

Key Responsibilities

• Design, develop, and deliver production-grade AI/ML solutions and platforms.
• Build agentic AI systems and LLM applications, including multi-agent workflows and tool integrations.
• Develop and optimise multi-step AI workflows and agent orchestration.
• Build AI evaluation frameworks and MLOps tooling for production environments.
• Design scalable APIs and microservices for high-throughput, low-latency workloads.
• Work with product managers, architects, engineers, and business stakeholders to translate

requirements into technical solutions.

• Participate in code reviews and promote engineering best practices.
• Implement secure coding, testing, monitoring, and governance practices.
• Mentor junior engineers and provide technical guidance.
• Drive AI solutions from system design through implementation and production deployment.

Technical Requirements

• 8+ years of software engineering experience, including at least 3+ years building AI/ML systems,

scalable ML infrastructure, or model-serving platforms.

• Strong proficiency in Python, particularly FastAPI and asyncio; experience with Go or Java is also

valuable.

• Production experience developing APIs and microservices.
• Hands-on experience developing Agentic AI and LLM applications, including:
• Multi-agent orchestration
• Tool use
• Planning frameworks
• Multi-step AI workflows
• Strong understanding of prompt engineering and RAG architectures.
• Experience with agentic frameworks such as:
• Strands
• LangGraph
• Google ADK
• Experience with LLM model serving, including vLLM and OpenAI-compatible APIs.
• Strong understanding of distributed systems, including:
• Microservices
• REST
• gRPC
• OpenAPI
• Event-driven architectures
• Strong AWS experience, including EC2/EKS, S3, IAM, RDS, and at least one AI service such as Bedrock

or SageMaker.

• Hands-on experience with Docker and Kubernetes.
• Experience with CI/CD and Infrastructure as Code, particularly CloudFormation or Terraform.
• Knowledge of observability tools such as OpenTelemetry and distributed tracing.

Preferred / Additional Skills

• Experience building AI evaluation frameworks, including agent benchmarking, reasoning traces, and

production monitoring.

• Experience with:
• Vector databases
• Knowledge graphs
• Semantic search
• AI memory systems
• Retrieval optimisation
• Experience implementing AI safety controls such as:
• Guardrails
• Content filtering
• Prompt injection detection
• PII redaction
• Knowledge of NIST AI RMF, MITRE ATLAS, and OWASP LLM Top 10.
• Familiarity with model fine-tuning techniques such as LoRA, RLHF, and supervised fine-tuning.
• Understanding of Responsible AI, model governance, and autonomous AI security.
• Knowledge of Zero Trust, Entra ID, OAuth2, and OIDC.
• Experience with GPU workload management and inference optimisation, particularly vLLM.
• Financial services or banking experience is advantageous.

Leadership & Stakeholder Skills

• Strong system-design and technical problem-solving capabilities.
• Ability to work independently with limited supervision.
• Strong communication and stakeholder management skills.
• Ability to collaborate with technical and non-technical stakeholders.
• Experience mentoring and guiding junior engineers.
• Ability to manage technical risks and contribute to governance and control frameworks.
• Strong understanding of enterprise technology and AI strategy.

Education

• Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related technical

discipline