New opportunity

DE-Manager-Python Tech Lead

EY · Bengaluru, India

About this role

The Python AI Tech Lead will drive the design, development, and delivery of digital solutions, specifically focusing on AI services, agent workflows, and pharmaceutical forecasting platforms. This role involves leading a team of engineers to build scalable, secure applications while collaborating with cross-functional stakeholders to translate business requirements into technical architectures.

Responsibilities

Lead and mentor a team of engineers in developing robust, scalable applications and defining best practices for coding, architecture, and deployment.
Design and develop Python-based AI services, agent workflows, APIs, and orchestration components.
Implement multi-agent frameworks including agent routing, tool integration, memory management, context handling, and human-in-the-loop capabilities.
Develop and integrate RAG pipelines, LLM applications, vector stores, prompt engineering frameworks, and evaluation mechanisms.
Build scalable integrations across Snowflake, AWS services, and AI/ML platforms.
Conduct code reviews, design reviews, and performance optimization to ensure production-grade delivery.
Develop and maintain pharmaceutical forecasts using analytical methods and partner with global teams to deliver insights.

Required qualifications

Bachelor’s or master’s degree in computer science, Engineering, or a related field.
7+ years of experience in software development.
At least 3 years of experience in a technical leadership role.
Proven experience in designing and deploying applications on AWS.
Strong understanding of database design and optimization.
Excellent problem-solving and decision-making skills.

Preferred qualifications

Experience with containerization tools such as Docker and Kubernetes.
Familiarity with Agile methodologies.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonFastAPIAWSPostgreSQLAI AgentsRAGLLMPrompt EngineeringSnowflakeCI/CDSQLTableauAlteryxDockerKubernetes

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Architecting Multi-Agent AI Frameworks
The role requires hands-on implementation of complex agent workflows, including routing, memory management, and human-in-the-loop controls. You must demonstrate the ability to design systems that handle context effectively while ensuring secure and reliable tool integration within an AWS environment.
Pharmaceutical Forecasting and Analytical Delivery
Beyond engineering, you are expected to maintain and optimize pharmaceutical forecasting models. This requires bridging the gap between technical AI implementation and domain-specific business needs, such as sensitivity modeling and collaborating with global marketing and finance teams.
Technical Leadership and Engineering Standards
You are responsible for defining coding, architecture, and deployment best practices. This involves mentoring a team of engineers, conducting rigorous design and code reviews, and ensuring that all AI services and RAG pipelines meet production-grade quality and security standards.
Scalable Integration and Data Orchestration
You must build and manage integrations across Snowflake, AWS, and various AI/ML platforms. Prioritizing performance and security in these data pipelines is critical to supporting the forecasting platforms and ensuring high-performance execution.

Likely challenges to think through

Balancing Technical Innovation with Domain Accuracy
The challenge lies in integrating cutting-edge LLM and RAG technologies with the high-precision requirements of pharmaceutical forecasting. Prepare by demonstrating how you validate AI outputs against historical trends and business logic to ensure the reliability of analytical insights.
Translating Complex AI Concepts for Non-Technical Stakeholders
You will frequently interact with global business teams (marketing, finance). The difficulty is communicating the risks, assumptions, and capabilities of AI agent workflows to stakeholders who may not have a technical background. Prepare by practicing how to frame technical architectural decisions in terms of business value and risk mitigation.
Managing Human-in-the-Loop Complexity
Implementing robust approval workflows and human-in-the-loop capabilities within automated agent systems is inherently difficult. Prepare by discussing your experience in designing interfaces or orchestration logic that allow for human intervention without compromising the scalability or performance of the automated system.

Practice your answers

Technical practice

Designing Multi-Agent Frameworks

What to coverExplain your approach to implementing agent routing and memory management in a multi-agent system. Discuss how you handle context persistence across agent interactions and ensure human-in-the-loop approval workflows are integrated without creating system bottlenecks.

RAG Pipeline Optimization

What to coverDescribe the components of a production-grade RAG pipeline you have built. Focus on how you manage vector store indexing, handle retrieval accuracy, and implement evaluation mechanisms to measure the quality of LLM-generated outputs.

Scalable AWS Architecture

What to coverHow do you design for scalability and security when deploying Python-based AI services on AWS? Address the use of managed services, VPC configurations, and how you ensure high availability for forecasting platforms.

Database Design for Forecasting

What to coverGiven the requirement for pharmaceutical forecasting, how do you approach schema design in PostgreSQL to handle complex analytical queries? Discuss strategies for performance optimization, such as indexing, partitioning, or caching, to support large datasets.

CI/CD for AI Applications

What to coverWhat is your strategy for implementing CI/CD pipelines for AI and LLM-based services? Suggestion: Focus on how you automate testing for non-deterministic model outputs and manage versioning for both code and model artifacts.

Containerization and Orchestration

What to coverHow do you leverage Docker and Kubernetes to manage the deployment of microservices and AI agents? Explain how you handle resource allocation and scaling for compute-intensive AI workloads in a production environment.

Prompt Engineering Frameworks

What to coverHow do you standardize prompt engineering across a team to ensure consistent model behavior? Discuss the use of templates, version control for prompts, and how you implement guardrails to ensure responsible AI usage.

Integration with Snowflake

What to coverDescribe your experience integrating Python applications with Snowflake for data-intensive forecasting. Address how you manage secure connectivity, data throughput, and the orchestration of data pipelines between AWS and Snowflake.

Behavioural practice

Technical Leadership and Mentorship

What to coverDescribe a time you mentored a team of engineers to improve their coding standards or architectural decision-making. Use the STAR method to explain the situation, the specific guidance you provided, and the resulting impact on team performance.

Translating Business Requirements

What to coverProvide an example of a time you had to translate complex business requirements from non-technical stakeholders into a technical architecture. Explain how you ensured the final solution met business goals while remaining technically sound.

Conflict Resolution in Design Reviews

What to coverTell me about a time you faced a disagreement during a design review regarding a critical architectural decision. How did you facilitate the discussion to reach a consensus while maintaining project timelines and quality standards?

Managing Project Delivery and Quality

What to coverDescribe a situation where you had to balance the need for rapid delivery with the requirement for high-quality, production-grade code. How did you manage stakeholder expectations and what trade-offs did you make?

Sharpen your knowledge

Topics to prepare

Multi-Agent Framework Architecture
Review the design patterns for agent routing, memory management, and human-in-the-loop workflows. Exercise: Draft a high-level architecture diagram for a system that manages state across multiple specialized AI agents, focusing on how context is passed and validated.
RAG Pipeline and LLM Evaluation
Understand the components of Retrieval-Augmented Generation, including vector store integration and prompt engineering frameworks. Exercise: Define a strategy for evaluating LLM output quality, specifically how you would measure accuracy and relevance in a pharmaceutical forecasting context.
Pharmaceutical Forecasting Methodologies
Familiarize yourself with analytical methods for short and long-range forecasting. Exercise: Prepare to explain how you would translate business assumptions and historical trends into a technical model using Python and SQL.

Skills to revise

Python and FastAPI Development
Focus on building scalable, production-grade APIs. Exercise: Create a modular FastAPI service that demonstrates asynchronous processing and structured error handling, which is critical for the AI services mentioned in the job description.
AWS Cloud Infrastructure
Focus on deploying and securing applications on AWS. Exercise: Design a deployment strategy using AWS services that ensures high availability and security for an AI-driven application, considering the need for scalable data integrations.
Database Design and Optimization
Focus on PostgreSQL and Snowflake performance. Exercise: Review complex SQL query optimization techniques and database schema design patterns that support high-volume analytical forecasting data.

Your preparation checklist

Prepare an AI Agent Architecture Walkthrough
Create a technical diagram and explanation of a multi-agent system you have designed or led. Focus on how you handled agent routing, memory management, and human-in-the-loop workflows, as these are core requirements for the role.
Develop a RAG Pipeline Case Study
Prepare a summary of a RAG application you built, highlighting your choice of vector stores, prompt engineering frameworks, and evaluation mechanisms used to ensure accuracy and performance in a production environment.
Draft a Technical Leadership Philosophy
Prepare concrete examples of how you have mentored engineers, conducted code reviews, and defined architectural standards. Be ready to discuss how you balance technical debt with the need for rapid delivery in an Agile environment.
Review Pharmaceutical Forecasting Methodologies
Familiarize yourself with standard analytical methods for short and long-range pharmaceutical forecasting. Prepare to discuss how you would integrate data from SQL, Tableau, or Alteryx into automated Python-based forecasting platforms.
Formulate Strategic Questions for the Interviewers
Prepare questions regarding the current maturity of their AI agent frameworks, the specific AWS services they prioritize for scaling, and how the engineering team collaborates with global stakeholders to translate business requirements into technical solutions.

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