New opportunity

Senior Associate - AI Platform Lead Engineer

TIAA Global Capabilities · Pune, India

About this role

The Senior Associate, AI Platform Lead Engineer (Foundation Engineering) will design, build, and optimize the core infrastructure and tooling powering AI agent systems and LLM/SLM applications within the AI Center of Excellence. This role sits at the intersection of AI engineering and backend systems design, focusing on scalable, production-grade foundations for enterprise AI initiatives. Key Responsibilities: Design and develop AI agent frameworks and orchestration layers. Apply LLM optimization techniques including quantization, distillation, prompt/context optimization, and caching to improve performance. Lead fine-tuning efforts for small language models (SLMs). Architect backend services and APIs for AI model integration. Design and deploy AI infrastructure on AWS. Implement Model Context Protocol (MCP) integrations for standardized tool and data access. Mentor junior engineers and contribute to engineering standards. Required Qualifications: Strong proficiency in Python with production-grade backend development experience. Experience with agent development frameworks like LangChain, LangGraph, and AutoGen. Proven experience designing enterprise-scale LLM solutions, multi-agent workflows, and advanced RAG architectures. Hands-on experience with LLM optimization and fine-tuning. Expertise in AWS cloud infrastructure (compute, storage, networking, security, CI/CD). Mandatory hands-on experience with Model Context Protocol (MCP). Strong communication and stakeholder management skills. Bachelor's degree required; Master's degree preferred. 5+ years of experience required, 7+ years preferred.

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

Skills for this role

PythonLangChainLangGraphAutoGenLLMSLMRAGModel Context ProtocolAWSCloud InfrastructureMachine LearningMicroservicesAPI DesignPrompt EngineeringQuantizationDistillation

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 Scalable AI Agent Infrastructure
The role requires designing and building orchestration layers and backend services that support enterprise-grade AI agents. You must demonstrate the ability to move beyond prototyping to create production-ready systems that integrate seamlessly with existing enterprise data and toolsets.
LLM and SLM Performance Optimization
A core priority is applying technical optimization techniques such as quantization, distillation, and fine-tuning to improve latency and cost-efficiency. You will need to show how you balance model performance with the practical constraints of enterprise deployment.
Standardizing Integration via Model Context Protocol (MCP)
Mandatory hands-on experience with MCP is required to standardize how agents access tools and data. You should be prepared to discuss how you implement these protocols to ensure consistent, secure, and scalable communication between AI models and backend infrastructure.

Likely challenges to think through

Balancing Innovation with Enterprise Standards
As an AI Platform Lead, you will face the challenge of adopting rapidly evolving agentic AI tools while maintaining strict engineering standards and documentation. Prepare by discussing your methodology for evaluating new research and tools against the stability requirements of a large financial services organization.
Managing Complex Multi-Agent Workflows
Designing advanced RAG architectures and multi-agent systems involves significant complexity in memory and context management. You may be challenged on how you handle state persistence and error recovery in distributed agent environments to ensure reliability for business-critical use cases.

Practice your answers

Technical practice

Designing Multi-Agent Orchestration Layers

What to coverExplain how you would architect an orchestration layer using frameworks like LangGraph or AutoGen. Focus on how you manage state, handle inter-agent communication, and ensure reliability in a production-grade environment.

Implementing Model Context Protocol (MCP)

What to coverDescribe your experience with MCP and how you would use it to standardize data and tool access for AI agents. Discuss the benefits of this protocol for decoupling agents from specific backend data sources.

LLM Optimization Techniques

What to coverCompare and contrast quantization, distillation, and prompt optimization for improving latency and cost. Suggestion: Be prepared to discuss when to apply each technique based on specific performance bottlenecks in an enterprise application.

Advanced RAG Architectures

What to coverDiscuss your approach to building robust RAG systems, specifically regarding context and memory management. Address how you handle retrieval accuracy and ensure the system remains scalable as the knowledge base grows.

Fine-Tuning Small Language Models (SLMs)

What to coverDetail your process for fine-tuning SLMs for domain-specific tasks. Cover the data preparation, selection of base models, and evaluation metrics used to ensure the model meets business requirements.

AWS Infrastructure for AI Services

What to coverExplain how you design and deploy scalable AI infrastructure on AWS. Focus on compute, storage, and networking considerations, as well as how you integrate CI/CD pipelines for model deployment.

Backend API Design for AI Integration

What to coverDescribe how you design microservices to support AI model integration. Emphasize security, API versioning, and how you ensure the backend can handle high-throughput requests from agentic workflows.

Evaluating Emerging AI Tools

What to coverHow do you stay current with the rapidly evolving agentic AI landscape? Explain your criteria for recommending the adoption of new research or tools into an existing enterprise engineering standard.

Behavioural practice

Mentoring Junior Engineers

What to coverDescribe a situation where you mentored an associate-level engineer. Focus on how you balanced providing guidance with allowing them to solve problems independently, and how you ensured code quality standards were met.

Stakeholder Management in AI Projects

What to coverProvide an example of how you translated complex technical AI requirements into business value for non-technical stakeholders. Explain how you managed expectations regarding project timelines or model performance.

Collaborating Across Organizational Boundaries

What to coverDescribe a time you worked with cross-functional teams to deliver a technical solution. Highlight how you navigated conflicting priorities and ensured the final product aligned with the broader organizational goals.

Handling Technical Debt in AI Systems

What to coverShare an experience where you had to balance the need for rapid AI innovation with the requirement for stable, production-grade infrastructure. How did you decide what to prioritize and how did you communicate this to leadership?

Sharpen your knowledge

Topics to prepare

Agentic AI Orchestration
Focus on the design of multi-agent workflows and orchestration layers. Exercise: Draft a system architecture diagram for a multi-agent system that handles complex document retrieval and summarization, ensuring you define clear hand-offs between agents. This is relevant as the role requires building agent frameworks for enterprise use cases.
LLM Optimization and SLM Fine-tuning
Review techniques for model efficiency, specifically quantization, distillation, and fine-tuning small language models. Exercise: Compare the trade-offs between quantization and distillation for a specific domain-specific task, documenting how each impacts latency and accuracy. This directly addresses the requirement for optimizing AI performance in production.
Advanced RAG Architectures
Study robust context and memory management systems within RAG pipelines. Exercise: Design a RAG workflow that incorporates persistent memory and context-aware retrieval to handle multi-turn user interactions. This is essential for the role's focus on enterprise-scale LLM solutions.

Skills to revise

Model Context Protocol (MCP)
Master the implementation of MCP for standardized tool and data access. Exercise: Build a simple MCP server that exposes a local database or API as a tool for an AI agent to query. This is a mandatory hands-on requirement for the role.
Python Backend Engineering
Ensure proficiency in building production-grade APIs and microservices. Exercise: Develop a scalable, containerized Python API that integrates with an LLM and includes logging, error handling, and CI/CD pipeline configuration. This is critical for the backend systems design aspect of the role.
AWS AI Infrastructure
Focus on deploying and securing AI services on AWS. Exercise: Create a Terraform or CloudFormation script to deploy a secure, scalable inference endpoint on AWS, including necessary networking and IAM security policies. This aligns with the requirement for designing and deploying AI infrastructure.

Your preparation checklist

Demonstrate Model Context Protocol (MCP) proficiency
Prepare a technical walkthrough of a project where you implemented MCP to standardize communication between an AI agent and external tools or data sources. Be ready to explain how this integration improved modularity and interoperability in your architecture.
Showcase enterprise-scale RAG and agentic workflows
Create a summary of a production-grade system you designed using LangChain, LangGraph, or AutoGen. Focus on how you managed memory, context, and multi-agent orchestration to solve complex business problems at scale.
Prepare LLM optimization and fine-tuning case studies
Document specific instances where you applied quantization, distillation, or fine-tuning to SLMs. Quantify the results in terms of latency reduction, cost savings, or performance improvements in a production environment.
Map AWS infrastructure expertise to AI deployment
Outline your experience designing cloud-native AI infrastructure on AWS. Specifically, prepare to discuss how you handle compute, networking, security, and CI/CD pipelines for AI services to ensure production-grade reliability.
Formulate questions for the AI Center of Excellence
Prepare thoughtful questions regarding the team's current AI roadmap, their approach to balancing proprietary vs. open-source model usage, and how they maintain engineering standards for agentic systems in a highly regulated financial services environment.

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