AI Lead developer

About the role

This position focuses on leading AI and frontend development to deliver innovative customer experiences. The role operates within a global, values-driven environment that emphasizes inclusion and continuous growth. You will shape technology solutions while collaborating across diverse teams. In this capacity, you will own the technical strategy for AI implementations and ensure that frontend architectures support intelligent, scalable applications. You will partner closely with product managers and business stakeholders to translate requirements into robust technical solutions. The role demands a balance between hands-on development and high-level system design. You will mentor team members and establish best practices for AI and frontend development. Ultimately, you will drive the delivery of high-impact products that leverage data and modern user interfaces.

Key facts

What you'll do

Analyze complex business problems and translate them into data strategies and frontend implementations using SQL and Python.

Construct KPI frameworks and dashboards, generating actionable insights that guide executive and operational decision-making.

Develop and deploy AI workflows, including RAG, agent-based systems, LLM-driven integrations, and scalable AI pipelines using modern MLOps practices.

Configure and manage private or offline AI environments, including the deployment and optimization of local LLM setups for secure operations.

Build and maintain high-performance backend AI services and APIs using Python and FastAPI to support scalable application needs.

Implement intelligent frontend user interfaces using ReactJS and Next.js, ensuring seamless interaction with AI-driven backend services.

Design and optimize data storage solutions, demonstrating proficiency in PostgreSQL and object storage platforms such as Azure Blob Storage, Azure Data Lake, or MinIO.

Create robust data models and pipelines that enable the generation of reliable business insights and reporting.

Leverage a background in Retail, Digital, or large-scale enterprise platforms to contextualize solutions for specific industry challenges.

Communicate complex technical and AI concepts in a clear, business-friendly manner to stakeholders with varying levels of technical literacy.

Requirements

You must possess extensive experience with AI workflows, including RAG, agent-based systems, LLM-driven integrations, and scalable AI pipelines.

You must have hands-on experience with private or offline AI setups, including the deployment and management of local LLM environments.

You must demonstrate strong backend development skills using Python and FastAPI for building AI services, APIs, and microservices.

You must have frontend exposure using ReactJS and Next.js to develop responsive and AI-integrated user interfaces.

You must have a good understanding of PostgreSQL and object storage platforms such as Azure Blob Storage, Azure Data Lake, or MinIO.

You must be capable of building KPIs, dashboards, and insights using SQL and Python to support strategic business initiatives.

You must have a background in Retail, Digital, or large-scale enterprise platforms to understand domain-specific requirements.

You must be able to explain complex AI and technical concepts in a simple, business-friendly manner to non-technical audiences.

Practical notes

Application reviews focus on skills, experience, and potential without requesting age, gender, marital status, or headshot. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Modern AI and frontend technology stacks power enterprise solutions in this role. Collaboration spans diverse, cross-functional teams that use cloud-native tools and practices. Continuous learning and agile ways of working shape day-to-day activities. The position balances independent problem solving with structured delivery expectations.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the location

India's tech capital offers some of the fastest internet in Asia at low cost. HSR Layout and Indiranagar concentrate coworking spaces. Monsoon season (Jun-Sep) brings heavy rain but year-round temps hover around 24°C. A large English-speaking population and startup scene make networking easy.