AI / Machine Learning Engineer at Deloitte

AI & Engineering leverages cutting-edge engineering capabilities to help build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These insights are powered by engineering for business advantage, helping transform mission-critical operations.

Join our AI & Engineering team in transforming technology platforms, driving innovation, and helping make a significant impact on our clients’ achievements. You’ll work alongside talented professionals reimagining and re-engineering operations and processes that are critical to business. Your contributions help Deloitte client service teams improve financial performance, accelerate new digital ventures, and fuel growth through innovation.

Key Responsibilities:

• Outcome-Driven Accountability: Embrace and drive a culture of accountability for customer and business outcomes. Develop engineering solutions that solve complex problems with valuable outcomes, ensuring high-quality, lean designs and implementations.
• Technical Leadership and Advocacy: Serve as the technical advocate for products, ensuring code integrity, feasibility, and alignment with business and customer goals. Lead requirement analysis, component design, development, unit testing, integrations, and support.
• Engineering Craftsmanship: Maintain accountability for the integrity of code design, implementation, quality, data, and ongoing maintenance and operations. Stay hands-on, self-driven, and continuously learn new approaches, languages, and frameworks with significant focus on infusing AI/ML/GenAI where possible/appropriate. Create technical specifications, and write high-quality, supportable, scalable code ensuring all quality KPIs are met or exceeded. Demonstrate collaborative skills to work effectively with diverse teams.
• Customer-Centric Engineering: Develop lean engineering solutions through rapid, inexpensive experimentation to solve customer needs. Engage with customers and product teams before, during, and after delivery to ensure the right solution is delivered at the right time.
• Incremental and Iterative Delivery: Adopt a mindset that favors action and evidence over extensive planning. Utilize a learning-forward approach to navigate complexity and uncertainty, delivering lean, supportable, and maintainable solutions.
• Cross-Functional Collaboration and Integration: Work collaboratively with empowered, cross-functional teams including product management, experience, and delivery. Integrate diverse perspectives to make well-informed decisions that balance feasibility, viability, usability, and value. Foster a collaborative environment that enhances team synergy and innovation.
• Advanced Technical Proficiency: Possess deep expertise in modern software engineering practices and principles, including AI/ML/GenAI, Agile methodologies and DevSecOps to deliver daily product deployments using full automation from code check-in to production with all quality checks through SDLC lifecycle. Strive to be a role model, leveraging these techniques to optimize solutioning and product delivery. Demonstrate understanding of the full lifecycle product development, focusing on continuous improvement and learning.
• Domain Expertise: Quickly acquire domain-specific knowledge relevant to the business or product. Translate business/user needs, architectures, and UX/UI designs into technical specifications and code. Be a valuable, flexible, and dedicated team member, supportive of teammates, and focused on quality and tech debt payoff.
• Effective Communication and Influence: Exhibit exceptional communication skills, capable of articulating complex technical concepts clearly and compellingly. Inspire and influence teammates and product teams through well-structured arguments and trade-offs supported by evidence. Create coherent narratives that align technical solutions with business objectives.
• Engagement and Collaborative Co-Creation: Engage and collaborate with product engineering teams at all organizational levels, including customers as needed. Build and maintain constructive relationships, fostering a culture of co-creation and shared momentum towards achieving product goals. Align diverse perspectives and drive consensus to create feasible solutions.

Qualifications

• A bachelor’s degree in computer science, software engineering, or a related discipline with a minimum of Second Class Upper/Upper Credit division. A Masters degree is an added advantage.
• Have a minimum of five credits in WASSCE Ordinary Level/NECO subjects or acceptable equivalent examination, including Mathematics and English Language in one sitting only.
• Strong software engineering foundation with deep understanding of OOPs, data-structure, algorithms, code instrumentations, beautiful coding practices etc.
•  Minimum of 5  years of experience with AI/ML, with last 2 years focused on GenAI as well as technologies like OpenAI, Claude, Gemini, LangChain, Agents, Vector databases, and approaches like Prompt Engineering, fine-tuning, etc.
• Proven experience in: Python, R, TensorFlow, PyTorch, Keras, Julia, ML libraries, NLP, etc.
• Proven experience with big data technologies, Angular, React, NodeJS, Python, C#, .NET Core, Java, Golang, SQL/NoSQL.
• Proven experience with cloud-native engineering, using FaaS/PaaS/micro-services on cloud hyper-scalers like Azure, AWS, and GCP.
•  Strong understanding of methodologies & tools like, XP, Lean, SAFe, DevSecOps, SRE, ADO, GitHub, SonarQube, etc.
• Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.