Applied AI Engineer

We are seeking an experienced Applied AI Engineer with proven experience embedding AI/ML technologies into enterprise software. You will play a critical role in building our MVP from the ground up, working closely with US and India teammates to design, develop, and ship features end-to-end. This is a unique opportunity for someone who thrives in an early-stage environment, enjoys the energy of building all new AI-enabled applications, and is motivated to shape both the product and the engineering culture.

Responsibilities:

• Full-stack delivery: Design, develop, and ship features across the data stack and apply enterprise SaaS best practices.
• AI integration: Transform user workflows to accelerate and simplify client tasks with AI and ML tooling.
• Product collaboration: Partner with Product and Design to translate customer workflows into performant, elegant product experiences.
• Technical quality: Own testing, reliability, observability, and security for the models and features you ship.
• Architecture and reviews: Contribute to system design and code reviews; help set engineering patterns that scale for AI.
• Customer focus: Engage with external users to validate assumptions and iterate quickly.
• Culture and collaboration: Model high ownership, humility, and teamwork; contribute to a strong engineering team culture and collaboration.

Requirements:

• Professional experience in engineering (enterprise SaaS preferred) and/or AI/ML products.
• Excellent communicator, comfortable collaborating across cultures and time zones.
• Hands - on experience integrating LLMs, vector databases, RAG pipelines, and AI-powered workflows.
• Solid data expertise: relational + NoSQL databases, data modelling, indexing, and performance optimisation.
• Proficiency with cloud platforms (AWS/Azure/GCP), containerization, CI/CD, and infrastructure-as-code.
• Track record of delivering in fast-moving, ambiguous environments.

Bonus:

• Experience with energy sector workflows (utility-scale renewables, data centres, grid infrastructure, etc. ).
• Strong proficiency in React + TypeScript on the front end, and python/Node.js on the back end.
• Background in geospatial data, enterprise document systems, or integrations.
• Familiarity with secure-by-default enterprise SaaS practices.