Technical Architect

Tech Lead / Architect – Python, Gen AI

Work Location

What Gramener offers you

Gramener will offer you an inviting workplace, talented colleagues from diverse backgrounds, career path, steady growth prospects with great scope to innovate. Our goal is to create an ecosystem of easily configurable data applications focused on storytelling for public and private use

Tech Lead / Architect – Python Applications with GenAI Integration

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 we are not looking for hands-on AI/ML development, candidates must demonstrate deep architectural understanding of the GenAI stack. A strong hands-on background in developing Python-based applications is essential.

Roles and 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.

Skills and Qualifications:

• 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 Sage Maker, 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.
• Direct data science work isn’t required, but a solid understanding of AI/ML engineering principles and pipeline design is essential to ensure the architecture supports model and data requirements.

About us

We help consult and deliver solutions to organizations where data is at the core of decision making. We undertake strategic data consulting for organizations in laying out the roadmap for data driven decision making, in order to equip organizations to convert data into a strategic differentiator. Through a host of our product and service offerings we analyse and visualize large amounts of data.