Artificial Intelligence (AI) - Internship

Selected intern's day-to-day responsibilities include:

1\. Develop and maintain AI agent systems using orchestration frameworks:

Build AI agents using frameworks like LangChain, LangGraph, AutoGen, and

Semantic Kernel, implement agent orchestration patterns for multi-agent

workflows, design agent architectures that handle complex reasoning tasks,

integrate different agent frameworks based on use case requirements, and

ensure agents can coordinate and communicate effectively.

2\. Work with foundation models and LLM integration: Integrate various LLM

providers (OpenAI GPT, Anthropic Claude, Groq, Gemini, Mistral, Llama) into

agent systems, implement model selection strategies based on task

requirements, handle API authentication, rate limiting, and error handling

across different providers, optimize prompt engineering for different model

capabilities, and manage costs and performance trade-offs between open-source

and closed-source models.

3\. Implement vector database and data storage solutions: Work with vector

databases (Pinecone, Weaviate, Milvus, Chroma, pgVector) for semantic search

and retrieval, design data storage architectures for embeddings and vector

representations, implement data ingestion pipelines for vector databases,

optimize vector similarity search performance, and manage data persistence and

retrieval for AI agents.

4\. Build API services with FastAPI and Python: Develop RESTful APIs using

FastAPI framework for AI agent endpoints, implement async/await patterns for

handling concurrent AI requests, create API endpoints for agent interactions

and tool execution, implement request validation and response serialization,

handle authentication and authorization for AI services, and ensure API

performance and scalability.

5\. Implement tool execution and external integrations: Develop tool execution

frameworks that allow AI agents to interact with external APIs and services,

integrate tools using frameworks like Composio and NPI, implement browser

automation and web scraping capabilities, create custom tools for specific

business logic, handle tool authentication and error handling, and ensure

secure tool execution.

6\. Design and implement memory management systems: Build conversational

memory systems using frameworks like Zep, Mem0, and Cognee, implement long-

term memory storage and retrieval for AI agents, design memory architectures

that support context retention across sessions, optimize memory usage and

storage costs, implement memory search and retrieval mechanisms, and handle

memory privacy and data management.

7\. Set up observability and monitoring: Implement observability tools

(Langfuse, Helicone, Datadog, Sentry) for AI agent monitoring, track agent

performance metrics, latency, and error rates, create dashboards for AI system

health monitoring, implement logging and tracing for agent workflows, analyze

AI model outputs and behavior patterns, and set up alerting for critical

issues.

8\. RAG and Retrieval Systems: Deep understanding of RAG architecture and

implementation, experience building RAG pipelines with vector databases and

LLMs, knowledge of document processing and chunking strategies, understanding

of embedding models and retrieval optimization, experience evaluating RAG

system performance, and ability to improve retrieval accuracy.

9\. Optimize production deployment, performance, and scaling: Deploy AI

systems to production environments with proper versioning and rollback

capabilities, optimize inference latency and throughput for real-time

applications, implement caching strategies for AI responses to reduce API

costs and improve response times, design scalable architectures that handle

high traffic loads, implement batch and streaming processing patterns.

10\. Collaborate with cross-functional teams: Work with backend developers to

integrate AI features into existing systems, coordinate with product team to

understand requirements and use cases, participate in architecture discussions

and technical planning, document AI workflows and system designs, share

knowledge about AI capabilities and limitations, and contribute to AI strategy

and roadmap planning.