Gramener Technology

2 weeks ago

Hyderabad, India Gramener Technology Solutions Private limited Full time

We are seeking a highly experienced Tech Lead / Architect to drive the architecture and delivery of modern, scalable data applications powered by Generative AI (GenAI). This role focuses on solution design, architectural leadership, and cross-functional collaboration across AI/ML platforms, backend engineering, and cloud-native infrastructure.While hands-on AI/ML development is not expected, candidates must demonstrate deep architectural understanding of the GenAI stack. A strong hands-on background in Python-based application development is essential.Key Responsibilities : - Lead the end-to-end architecture and design of Python-based applications integrated with GenAI capabilities.- Translate business and product requirements into modular, scalable, and maintainable technical solutions.- Guide and mentor backend, infrastructure, and MLOps teams in implementing architecture blueprints.- Evaluate and recommend the right mix of tools, frameworks, and platforms including LLM providers, vector databases, and cloud-based ML services.- Ensure the security, scalability, and performance of all deployed systems.- Stay current with advancements in GenAI, and proactively bring relevant capabilities into solution design.Required Experience : - 8+ years of experience in software engineering, with a minimum of 4 years in a technical leadership or architecture role.- Proven track record in building robust Python-based backend systems, using frameworks such as FastAPI, Flask, or Django.- Expertise in microservices architecture, distributed system design, and integration patterns.- Strong familiarity with cloud platforms such as AWS, Azure, or GCP, and practical knowledge of AI/ML services like SageMaker, Vertex AI, or Azure ML.- Architectural experience with vector databases (e.g., Pinecone, FAISS, Weaviate, Qdrant) for semantic search and RAG pipelines.- Understanding of DevOps practices, including Docker, Kubernetes, and infrastructure as code.- Knowledge of CI/CD processes, system security, and observability/monitoring frameworks.GenAI & AI/ML Architecture Expertise : - Sound understanding of GenAI system components, including LLM lifecycles, embedding generation, prompt orchestration, and RAG architectures.- Ability to architect complete GenAI workflows, from context ingestion and enrichment to inference handling and post-processing.- Familiarity with API orchestration, agent-based design patterns, and semantic search strategies.- While direct data science work is not required, a strong grasp of AI/ML engineering principles and pipeline design is expected to ensure architecture aligns with model and data needs. (ref:hirist.tech)

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