GEN AI Sr Lead

What matters most

• End-to-End Generative AI Solution Delivery

  The role demands ownership of the full AI lifecycle, from initial POC development and architecture design to production deployment on GCP. You must demonstrate the ability to translate complex business requirements into scalable, reliable AI systems using LLMs, RAG, and vector databases.

• Technical Leadership and Mentorship

  As a Senior Lead, you are expected to guide architecture discussions and mentor development teams. This requires balancing hands-on coding in Python and FastAPI with the ability to drive best practices, ensure code quality, and provide technical direction to junior team members.

• GCP and Cloud-Native AI Architecture

  Proficiency in GCP, specifically Vertex AI, is a mandatory requirement. You must be capable of architecting solutions that leverage microservices, serverless architecture, Docker, and Kubernetes to ensure that AI models are performant and secure in enterprise-scale production environments.

Likely challenges to think through

• Balancing Rapid Prototyping with Production Scalability

  A common challenge in GenAI roles is moving from a successful POC to a production-ready system. You should prepare to discuss how you handle technical debt, model latency, and cost management when scaling RAG-based applications in a cloud environment.

• Managing Stakeholder Expectations in AI Projects

  Given the client-facing nature of the role, you may face pressure to deliver results on aggressive timelines while managing the inherent uncertainty of AI model performance. Prepare to explain your approach to communicating technical limitations and setting realistic milestones for AI initiatives.

Topics to prepare

• Generative AI Architecture and RAG

  Review the end-to-end architecture of Retrieval-Augmented Generation (RAG) systems. Exercise: Design a high-level architecture diagram for a document-based Q&A system, detailing the ingestion pipeline, vector database selection, and retrieval strategy. This is relevant to the role's requirement for designing scalable GenAI solutions.

• Agentic AI Frameworks

  Study the conceptual design of autonomous agents, including planning, tool use, and memory management. Exercise: Compare the workflow of a standard LLM chain versus an agentic framework for a multi-step task. This aligns with the explicit requirement for experience with Agentic AI frameworks.

• GCP Vertex AI Ecosystem

  Deepen understanding of Google Cloud's AI services, specifically model deployment, tuning, and monitoring within Vertex AI. Exercise: Outline the steps to deploy a custom LLM on Vertex AI and configure its monitoring for performance and drift. This is critical as GCP/Vertex AI is a mandatory requirement.

Skills to revise

• Python API Development with FastAPI

  Practice building robust, secure APIs using FastAPI. Exercise: Create a FastAPI endpoint that integrates an LLM call with authentication and input validation. This directly supports the role's need for API development, security, and Python proficiency.

• Containerization and Orchestration

  Develop proficiency in deploying AI models using Docker and Kubernetes. Exercise: Write a Dockerfile for a Python-based AI service and define a basic Kubernetes deployment manifest. This is essential for the role's responsibility of building and deploying models in production environments.

• Vector Database Implementation

  Gain hands-on experience with vector search technologies. Exercise: Implement a script to generate embeddings for a dataset and perform a similarity search using a vector database. This is relevant to the preferred qualification for AI search and vector database expertise.

• Prepare a RAG and Agentic AI project walkthrough

  Develop a concise presentation of a past project where you implemented Retrieval-Augmented Generation (RAG) or Agentic AI frameworks. Focus on your architectural decisions, the specific LLMs used, and how you ensured scalability and reliability in a production environment.

• Document your GCP and Vertex AI expertise

  Create a summary of your experience deploying AI models on Google Cloud Platform. Specifically, highlight your proficiency with Vertex AI, serverless architectures, and how you managed security and authentication for enterprise-scale AI deployments.

• Draft technical leadership scenarios

  Prepare examples of how you have mentored development teams and led architecture discussions. Be ready to explain how you translated complex business requirements into technical AI solutions using Python, FastAPI, or Flask.

• Formulate strategic questions for the interview

  Prepare questions regarding Birlasoft's current AI roadmap, such as how they approach the balance between off-the-shelf LLMs and custom model fine-tuning, or how they manage the lifecycle of AI initiatives from POC to production.