SME for the AI and Automation team. This role will lead the exploration, assessment, prototyping, and implementation of advanced AI, RPA, and Hyperautomation solutions across the enterprise. The role will evaluate business use cases, design and build proofs of concept (POCs), define the technology stack and engineering standards, and mentor a growing team of automation engineers as we scale our automation journey.
This role bridges AI engineering, platform engineering, automation architecture, and applied innovation, ensuring the safe, secure, scalable, and value-driven adoption of emerging technologies that align with business strategy and governance requirements.
Key Responsibilities
- Use-Case Discovery & Technical Leadership
- Assess, prioritize, and validate AI, RPA, and hyperautomation use cases with business and IT stakeholders.
- Lead rapid prototyping and POC / pilot development to demonstrate feasibility and business value.
- Translate conceptual ideas into technical designs, solution architectures, and implementation roadmaps.
- Act as the enterprise SME for Generative AI, LLMs, Intelligent Automation, and orchestration technologies.
- AI & Hyperautomation Engineering
- Design, develop, and deploy AI-enabled automation solutions, including:
- Advanced prompting and RAG pipelines
- AI application orchestration and agents
- Multimodal and LLM-driven workflows
- RPA workflows, intelligent document processing, task mining, and process automation
- Build reusable components, frameworks, and accelerators to industrialize automation delivery.
- Ensure observability, performance, resilience, and optimization across deployed automation assets.
- Platform, Technology Stack & Standards
- Define and evolve the technology stack, architecture patterns, coding standards, and best practices for:
- AI engineering platforms
- RPA platforms and hyperautomation suites
- Data and integration layers required for automation
- Partner with Security, IAM, Data, and Infrastructure teams to embed:
- Guardrails, LLM security, IAM controls, governance, and risk management
- Evaluate and select vendors, tools, and platforms; lead technical due diligence and benchmarking.
- Center-of-Excellence & Mentorship
- Mentor and upskill automation engineers, citizen developers, and cross-functional teams.
- Contribute to the foundation of a Hyperautomation / AI Center of Excellence (CoE).
- Establish engineering playbooks, reusable design patterns, and technical documentation.
- Collaboration & Delivery
- Work closely with Enterprise Architecture, Cybersecurity, Data Engineering, and Business Process teams.
- Support production rollout, handover, and lifecycle management of automation solutions.
- Track metrics, ROI, and value realization of automation initiatives.
Qualifications & Experience
- Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related field.
- 5+ years of experience in AI, Automation, Software Engineering, or Intelligent Automation delivery.
- Hands-on experience with at least two of the following:
- LLMs, RAG, vector databases, AI agents, model integration
- RPA platforms (UiPath, Power Automate, Automation Anywhere, etc.)
- Workflow orchestration / BPM / event-driven automation
- Strong background in:
- Python, Node.js, or equivalent engineering languages
- APIs, microservices, and system integrations
- Data pipelines, preprocessing, and observability concepts
- Experience working in enterprise or large-scale business technology environments.
- Excellent communication, technical leadership, and stakeholder-engagement skills.
Preferred / Advantageous
- Exposure to AI security, IAM guardrails, or governance models
- Cloud platforms (Azure, AWS, GCP) and MLOps or AIOps tooling
- Experience leading engineering teams or mentoring developers
- Success Outcomes (First 12 Months)
- Establish the foundation of the AI & Hyperautomation engineering capability.
- Deliver at least 3-5 high-impact automation POCs converted into production deployments.
- Define and publish the technology stack, engineering standards, and platform roadmap.
- Mentor automation engineers into a capable delivery team.
- Demonstrate measurable business value and operational efficiency improvements.
