Technical Lead

What matters most

• Production-Grade Agentic Orchestration

  The role demands architecting complex, stateful multi-agent workflows using LangGraph and LangChain. You must prioritize building reliable systems that handle state management, checkpoints, and human-in-the-loop interactions, ensuring that agentic behaviors remain predictable and scalable in a production environment.

• Advanced RAG Pipeline Engineering

  A core priority is the design of high-quality RAG pipelines that go beyond basic implementation. This includes optimizing chunking strategies, managing vector stores like Pinecone or OpenSearch, and implementing advanced techniques such as query rewriting and reranking to ensure retrieval accuracy.

• Secure and Compliant AWS Infrastructure

  You are expected to deploy GenAI solutions on AWS while strictly enforcing PII/PHI controls and security best practices. This involves managing IAM least privilege, VPC configurations, and encryption, alongside implementing guardrails to prevent jailbreaks and ensure content moderation.

• Rigorous Evaluation and Observability

  The role requires establishing a feedback loop for model performance. You must prioritize building test datasets and implementing both offline and online evaluation frameworks using tools like LangSmith or MLflow to track latency, token usage, and answer relevance.

Likely challenges to think through

• Balancing Latency and Model Complexity

  Designing agentic workflows often introduces significant latency. The challenge lies in optimizing model selection, context windows, and orchestration logic to meet performance requirements without sacrificing the accuracy or reasoning capabilities of the AI application.

• Managing Non-Deterministic System Behavior

  GenAI applications are inherently probabilistic, which complicates standard CI/CD and testing processes. You will face the challenge of creating robust unit and contract tests for systems that do not produce identical outputs, requiring a shift toward statistical evaluation and versioned prompt management.

• Technical Debt in Rapidly Evolving AI Stacks

  The GenAI ecosystem changes rapidly, making it difficult to maintain long-term stability. You must prepare to manage technical debt by creating modular, well-documented architectures that allow for the swapping of foundation models or orchestration frameworks without requiring a complete system rewrite.

Topics to prepare

• Agentic Workflow Architecture

  Focus on designing stateful multi-agent systems using LangGraph. Exercise: Draft a system architecture diagram for a multi-agent workflow that includes human-in-the-loop checkpoints and error-handling policies. This is essential for the role's requirement to build production-grade agentic applications.

• RAG Pipeline Optimization

  Deepen understanding of advanced retrieval techniques including query rewriting, reranking, and chunking strategies. Exercise: Compare the performance impact of different chunking strategies on retrieval accuracy for a specific dataset. This directly addresses the requirement to design high-quality RAG pipelines.

• MLOps and Observability

  Study the lifecycle of LLM applications from offline evaluation to online monitoring. Exercise: Design an evaluation framework using LangSmith or MLflow to track latency, token usage, and faithfulness for a RAG pipeline. This is critical for the role's focus on measurable outcomes and model performance.

Skills to revise

• LangGraph and LangChain Implementation

  Practice building complex chains and graphs with robust I/O schemas. Exercise: Create a LangGraph workflow that uses tool calling to perform a multi-step task with state persistence. This is a core technical requirement for the advertised position.

• AWS Infrastructure as Code

  Focus on deploying scalable AI services using Terraform and AWS services like Bedrock and EKS. Exercise: Write a Terraform script to provision an AWS Lambda function that interacts with a Bedrock model endpoint securely. This is necessary for the role's requirement to ship scalable solutions on AWS.

• Backend Microservices with FastAPI

  Develop skills in building production-ready APIs with Pydantic for data validation. Exercise: Build a FastAPI service that implements rate limiting, caching, and asynchronous job processing for an LLM inference endpoint. This aligns with the engineering excellence requirement.

• Architect a Multi-Agent Workflow Walkthrough

  Prepare a technical deep-dive into a project where you used LangGraph or LangChain to build a stateful agentic workflow. Be ready to explain your choice of nodes, edges, state management, and how you implemented human-in-the-loop checkpoints to handle complex, multi-step tasks.

• Demonstrate RAG Pipeline Optimization

  Create a summary of your experience designing RAG systems, specifically focusing on how you handled chunking strategies, reranking, and query rewriting. Prepare to discuss how you evaluated retrieval quality and managed vector stores like Pinecone or Amazon OpenSearch.

• Outline an AWS Production Deployment Strategy

  Draft a high-level architecture diagram for deploying a GenAI application on AWS. Include how you would utilize ECS/EKS, Bedrock, and SageMaker while ensuring security through IAM, VPC, and KMS, and managing costs via effective resource monitoring.

• Prepare an MLOps and Evaluation Framework

  Document your approach to model observability and evaluation. Explain how you use tools like LangSmith or MLflow to track latency, token usage, and accuracy, and describe your process for running offline evaluations and online A/B tests for model performance.

• Formulate Questions on Governance and Scaling

  Prepare thoughtful questions regarding how the team handles PII/PHI compliance in GenAI workflows, their current strategy for managing technical debt in rapidly evolving AI stacks, and how they balance model accuracy with latency and cost efficiency at scale.