Sparkd - AI for Business Growth

Machine Learning Engineer

• At Gemmo, interns work on real production projects not toy datasets or internal tools. Depending on your profile and interests, you could be placed on one of two flagship tracks:
• - Track 1. AI for Financial Services- You'll work on Machine Learning solutions for one of the most data-rich industries in the world. Problems you might tackle include prediction models, document analysis with ML, fine-tuning LLMs for conversational data interfaces, and extracting actionable insights from large-scale datasets.
• - Track 2. Computer Vision for Pharma- You'll contribute to Computer Vision pipelines deployed in pharmaceutical environments, think object tracking, behaviour understanding, and solving complex real-world problems with nothing but a camera and a well-trained model.
• Both tracks involve close collaboration with senior engineers and direct exposure to enterprise clients. This is not a support role you'll be expected to contribute from day one.
• You'll gain hands-on experience working alongside senior ML engineers on real production problems. Not simulations, not tutorials code that runs on enterprise systems.
• Build Machine Learning models with financial data
• Design, build, and maintain CRUD APIs to interact with users and serve the models
• Deploy, monitor, and maintain applications in Azure and Snowflake

Machine Learning Engineer, Intern to Full-Time

Engineering Manager

• The Head of Engineering is the de facto technical team lead for Gemmo’s software development function.
• You will make real technical decisions, review real code, and have real conversations with clients about architecture.
• Lead and coach a cross-functional team of researchers, backend developers, and data scientists — acting as the team’s technical conscience.
• Own end-to-end software delivery: system design, code quality, testing discipline, PR reviews, and deployment practices.
• Design and document cloud architecture for client projects (primarily Azure), taking responsibility for service selection, cost optimisation, and scalability.
• Manage direct relationships with technical stakeholders at client organisations, translating business requirements into clear engineering plans.
• Liaise with the Chief AI Officer on AI system design, evaluation frameworks (train/validation/test governance), and product direction.
• Establish and enforce engineering best practices across the team: coding standards, branch strategy, CI/CD, monitoring.
• Identify and unblock technical risks in ongoing projects, particularly multi-agent pipelines and LLM-integrated products.
• Hire, onboard, and mentor software developers; maintain a culture of technical excellence and intellectual curiosity.
• Contribute to technical proposals and scoping documents for new client engagements.

Head of Engineering

Cloud Engineer

• Design and document end-to-end cloud architectures on Azure for financial services clients, taking ownership of service selection, cost optimisation, and compliance considerations.
• Act as the primary technical point of contact with client engineering and infrastructure teams, leading architecture discussions and translating business requirements into concrete technical plans.
• Work alongside ML engineers and backend developers to ensure that AI workloads, data pipelines, and APIs are deployed on infrastructure that meets enterprise and regulatory requirements.
• Contribute to technical proposals and scoping documents for new client engagements, providing credible architectural estimates and risk assessments.
• Identify and resolve infrastructure risks across active projects, particularly where data governance, latency, or compliance constraints are involved.

Senior Azure Cloud Architect

• At Gemmo, interns work on real production projects not toy datasets or internal tools. Depending on your profile and interests, you could be placed on one of two flagship tracks:
• Track 1. AI for Financial Services - You'll work on Machine Learning solutions for one of the most data-rich industries in the world. Problems you might tackle include prediction models, document analysis with ML, fine-tuning LLMs for conversational data interfaces, and extracting actionable insights from large-scale datasets.
• Track 2. Computer Vision for Pharma - You'll contribute to Computer Vision pipelines deployed in pharmaceutical environments, think object tracking, behaviour understanding, and solving complex real-world problems with nothing but a camera and a well-trained model.
• Both tracks involve close collaboration with senior engineers and direct exposure to enterprise clients. This is not a support role you'll be expected to contribute from day one.
• You'll gain hands-on experience working alongside senior ML engineers on real production problems. Not simulations, not tutorials code that runs on enterprise systems.
• Build Machine Learning models with financial data.
• Design, build, and maintain CRUD APIs to interact with users and serve the models.
• Deploy, monitor, and maintain applications in Azure and Snowflake.

Machine Learning Engineer, Intern to Full-Time

Head of Engineering

Senior Azure Cloud Architect

Machine Learning Engineer Intern – Full-Time Track