Intelligent applications powered by AI, machine learning, and automation are becoming integral to business operations. However, deploying and supporting these applications requires a combination of specialised skills and well-defined roles. Below, we outline the core positions and competencies essential for successfully implementing and maintaining intelligent applications.
Core Roles in Deploying and Supporting Intelligent Applications
1. AI/ML Engineer
AI and machine learning engineers design and develop the core algorithms that power intelligent applications. Their responsibilities include:
- Building and training machine learning models
- Fine-tuning AI algorithms for efficiency and accuracy
- Implementing natural language processing (NLP), computer vision, and deep learning models
- Optimising AI models for deployment at scale
2. Cloud & DevOps Engineer
Intelligent applications rely on cloud infrastructure and automation for scalability and reliability. This role ensures smooth AI workload operation by:
- Designing and managing cloud environments (Azure, AWS, or Google Cloud)
- Implementing security best practices
- Automating infrastructure with Infrastructure as Code (IaC) tools
- Setting up CI/CD pipelines and managing containerised AI applications
3. Software Engineer (AI Application Development)
Software engineers integrate AI capabilities into user-friendly applications. Their responsibilities involve:
- Developing APIs and front-end interfaces for AI-powered solutions
- Integrating AI models using frameworks like TensorFlow, PyTorch, or OpenAI APIs
- Ensuring robust and scalable application architecture
4. Data Engineer
Data engineers design and maintain data pipelines that feed AI models. Their key responsibilities include:
- Building and managing ETL (Extract, Transform, Load) pipelines
- Ensuring data quality, integrity, and governance
- Managing databases, data lakes, and real-time data processing systems
5. AI Product Manager
AI product managers ensure that intelligent applications align with business goals and deliver value. Their responsibilities include:
- Defining AI product roadmaps and success metrics
- Identifying AI use cases and aligning them with business needs
- Collaborating with technical teams to prioritise features
Options for Sourcing These Roles
Organisations can build their AI teams in multiple ways depending on their specific needs, budget, and existing talent:
1. In-House Hiring
- Recruit AI specialists, engineers, and data experts to build a dedicated team.
- Provides full control but requires long-term investment in talent acquisition and training.
2. Partnering with AI Consultants and Solution Providers
- Engage with specialised AI and cloud consulting firms like Playtime Solutions to augment expertise.
- Access to experienced professionals without long-term commitments.
3. Outsourcing Development and Support
- Work with offshore or nearshore AI development firms.
- Cost-effective for companies looking for skilled professionals without full-time hiring.
4. Upskilling Existing Teams
- Invest in AI and cloud training for current employees.
- Utilise online courses, workshops, and Microsoft AI certifications.
5. Using Managed AI Services
- Leverage AI-as-a-Service platforms and managed solutions.
- Reduces the complexity of AI deployment and maintenance.
Conclusion
Deploying and supporting intelligent applications requires a multidisciplinary team with expertise in AI, data engineering, cloud computing, and software development. Businesses that invest in these roles and skill sets will be better positioned to leverage AI-driven solutions for competitive advantage and operational efficiency.
By exploring different sourcing options—whether through hiring, consulting, outsourcing, or managed services—businesses can ensure they have the right expertise to successfully deploy and support intelligent applications.
Does your organisation have the right team to deploy and support intelligent applications? If not, let’s discuss how together we can accelerate your AI adoption and ensure long-term success.
