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Tier-1 brand, mid-level backend+AI role with common title and metro appeal increases applicant density.
Backend engineering with AI/LLM specialization is transferable across industries but retains domain specificity.
Explicit 3-year requirement plus mandatory backend, Python, LLM, cloud, and Kubernetes skills raise shortlisting strictness.
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Build and scale high-performance backend microservices and APIs using Python frameworks such as FastAPI, Django, or Flask.
Integrate and optimize AI capabilities including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI workflows, and vector search into enterprise software solutions.
Ensure production reliability through automated deployments, testing (unit, integration, API), containerization with Docker/Kubernetes, and operate on cloud platforms (AWS, Azure, or GCP).
Minimum 3 years professional experience in software engineering focused on backend services and AI integration.
Proficiency in Python (object-oriented and asynchronous programming) and experience building RESTful or gRPC APIs using FastAPI, Django, or Flask.
Experience with relational databases (PostgreSQL or MySQL), NoSQL, caching (Redis), ORMs (SQLAlchemy or Django ORM), and schema migrations.
Experience integrating LLM APIs or open-source AI models, implementing AI orchestration, RAG, vector search, and hybrid-query workflows; familiarity with Git, CI/CD, Docker, Kubernetes, and at least one major cloud platform (AWS, Azure, or GCP).
Experienced backend engineer with demonstrated delivery of scalable, low-latency microservices supporting AI-enabled enterprise applications.
Comfortable working on AI integration at the intersection of machine learning models (especially LLMs) and backend engineering, with a focus on real-world deployment and reliability.
Capable of collaborating across engineering leadership and cross-functional teams, with operational expertise in monitoring, incident triage, performance profiling, and container/cloud environments.