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Senior Associate, Core Technology

GALE · galepartners.com
BengaluruHybridSenior4–7 yrs
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Straight from the company’s own careers site.

The job in short

Back-end web and data platform engineering with RAG/LLM integrations, leading sprints.

PythonReactAngularVueDjangoLLMRAGAgenticFlaskAWSLangChainAirflow

Details

Found
37 d ago · posted 2026-07-22
Location
Bengaluru

Full description

GALE is a creative media consultancy that brings business insights to brand storytelling and activates it across every channel. With expertise in business strategy, media, CRM, addressable and integrated brand communications, performance marketing, and technology, GALE creates marketing systems and communications that grow businesses. GALE delivers strong outcomes for its partners in automotive, QSR, retail, gaming, entertainment, telecom and more. The agency currently has offices in New York, Detroit, Singapore, Toronto, Austin, Los Angeles, London, and Bengaluru. Our teams are connected by a set of core values that inform everything we do, from how we hire to how we work together: values like Everyone Matters, No Silos, and Masters of Our Craft. If you’re driven by a passion to build something great, a desire to innovate, and a commitment to achieve excellence in your craft, GALE is a great place for you. About the role -- As a Senior Associate, 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 (No day is the same) • 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 • 4–7 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

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