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Tier-1 brand, metro location, mid-level AI title, and broad skillset requirements raise competition.
Deep LLM, RAG, model-serving, and production ML experience required, limiting cross-domain transferability.
Explicit years, mandatory production ML experience, and specific tech stack make shortlisting strict.
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Own end-to-end lifecycle of large language model (LLM) powered applications and AI-driven features, from design to deployment and continuous optimization.
Design, develop, and deploy production-grade LLM, multimodal AI, and generative AI solutions, including Retrieval-Augmented Generation (RAG) pipelines and AI agent integration into enterprise systems.
Collaborate with cross-functional teams to translate business requirements into scalable, secure, and measurable AI solutions driving business impact.
5-7 years of software engineering experience with 2-3 years focused on designing, deploying, and operating AI/ML production solutions.
Strong proficiency in Python and solid software engineering fundamentals including API development, distributed systems, and model serving.
Proficiency with PostgreSQL; experience with AWS cloud-native AI platforms (e.g., Bedrock, SageMaker) and AI/ML frameworks like PyTorch, LangChain, LlamaIndex, or equivalents.
Experience working with large language models, generative AI technologies, Retrieval-Augmented Generation (RAG), prompt engineering, and AI application development.
Experienced in architecting and operating complex AI systems in production with accountability for reliability and performance.
Strong hands-on expertise in integrating AI agents and multimodal models into scalable enterprise backend services and APIs.
Capable of driving technical decisions that align with strategic objectives and deliver measurable business outcomes in dynamic environments.