Team Lead, Core Technology

GALE; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

GALE is seeking a Team Lead for Core Technology in Bengaluru. The role involves leveraging technical skills to build high-performance web applications, focusing on backend development primarily using Python/Django. Candidates should have 6+ years of experience and a strong foundation in AWS and AI engineering.

Expect to design scalable systems, enhance backend services, and implement AI workflows. The ideal candidate thrives in an agile environment and is proficient in modern engineering practices.

Qualifications

• 6+ years of experience in Python with deep expertise in Django.
• Solid experience with AWS in production environments.
• Hands-on experience in building AI/LLM solutions.

Responsibilities

• Design technical designs for new capabilities and improvements.
• Build and enhance backend services and APIs in Python/Django.
• Support data modeling and query optimization for relational databases.
• Implement custom RAG workflows and LLM integrations.
• Contribute to building data pipelines using AWS tools.
• Write code that adheres to engineering best practices.

Skills

Python

Django

AWS

AI Engineering

TDD

Data Pipelines

Tools

GitHub

PostgreSQL

Airflow

Job description

About the role

As a Team Lead, Core Technology, you’ll leverage your technical depth and problem‑solving skills to build reliable, lightweight, high‑performing web applications and platform services. While your strength is back‑end development (Python/Django and related frameworks), you’re comfortable collaborating across the stack and are conversant with modern front‑end ecosystems (React/Angular/Vue).

You thrive in an agile, sprint‑driven environment and bring a strong engineering mindset: clean architecture, test‑driven development, automated testing, CI/CD, and scalable system design. You’re also excited by the evolution of AI engineering – especially RAG (Retrieval‑Augmented Generation), LLM integrations, and agentic workflows. You can take a problem from ambiguity to delivery—design → build → test → deploy → iterate.

What you might do

• Design: Create technical designs (HLD/LLD) for new capabilities and improvements, ensuring solutions are scalable, secure, and maintainable.

• Backend engineering (Python): Build and enhance backend services and APIs primarily in Python/Django (and related frameworks as needed), focusing on performance, reliability, and clean architecture.

• Database & data layer: Work with relational databases (e.g., PostgreSQL/Redshift) and support data modeling, query optimization, and production readiness.

• AI/RAG & Agent work: Design and implement custom RAG workflows and LLM integrations—covering retrieval, prompt orchestration, grounding, and connecting to vector stores (e.g., Pinecone/FAISS) where applicable.

• Data pipelines: Contribute to building/operating data pipelines and jobs using AWS and open‑source tools (e.g., EMR/Glue/Redshift, Airflow/Spark) — including orchestration, monitoring, and reliability improvements.

• Engineering excellence: Write code that holds up in TDD, build automated tests, participate in reviews, and support CI/CD practices using tools like GitHub and JIRA.

• Sprint ownership & leadership: Independently run sprints for a small team—planning, task breakdown, execution tracking, and clear communication with internal stakeholders and clients.

Who you are

• 6+ years of strong experience in Python, with deep expertise in Django and other frameworks.

• Strong backend foundation and familiarity with frameworks such as Django / Flask / Pyramid (or equivalents).

• Solid experience working with AWS (or similar cloud platforms) in production environments.

• Strong understanding of:

  • HLD/LLD, system design fundamentals, API design, and scalable architectures
  • Engineering best practices: testing, code quality, documentation, and maintainability

• Hands‑on experience building AI/LLM solutions, including:

  • RAG pipelines and agent workflows
  • Tools/frameworks such as LangChain, Pydantic, and related ecosystems
  • Familiarity with AI agent design patterns and best practices