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Gramener Technology - Technical Architect - Generative AI

Gramener Technology - Technical Architect - Generative AI

Gramener Technology Solutions Private limitedHyderabad
30+ days ago
Job description

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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