AI for SDLC Architect

4 weeks ago

Hyderabad, India Pepsico Full time

Overview

Design, develop, and optimize autonomous AI agents that interact with systems, users, and APIs to perform complex tasks. You will work at the intersection of LLMs, agentic frameworks, and cloud architectures, developing self-improving AI workflows for automation, coding, and decision-making.

Responsibilities

- Strong Python Programming experience with AI/ML frameworks such as TensorFlow, PyTorch.
- Design, build, and deploy AI agents using LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks.
- Integrate large language models (LLMs) (GPT-4,o1, Claude, Llama, Mistral etc.,) into multi-agent systems.
- Develop multi-agent orchestration pipelines, enabling collaboration between AI agents for complex task execution.
- Knowledge of vector databases and embeddings for retrieval-augmented AI (RAG).
- Implement retrieval-augmented generation (RAG) with vector databases like Milvus, Pinecone, Azure AI Search (with Vector Indexing).
- Optimize AI workflows using Reinforcement Learning (RLHF) and function calling for improved reasoning and automation.
- Integrate AI agents with APIs, DevOps tools and existing workflows.
- Ensure observability, debugging, and monitoring of AI agent behavior through logging frameworks.
- Implement memory persistence for long-term agent interactions using graph-based memory or event stores.
- Collaborate with cross-functional teams to embed AI agents into DevOps, automation, and software development processes.
- Implement various prompt techniques, including zero-shot, few-shot, chain-of-thought (CoT), and fine-tuning for optimized LLM performance

Qualifications

BE/B-Tech

BE/B-Tech

- Strong Python Programming experience with AI/ML frameworks such as TensorFlow, PyTorch.
- Design, build, and deploy AI agents using LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks.
- Integrate large language models (LLMs) (GPT-4,o1, Claude, Llama, Mistral etc.,) into multi-agent systems.
- Develop multi-agent orchestration pipelines, enabling collaboration between AI agents for complex task execution.
- Knowledge of vector databases and embeddings for retrieval-augmented AI (RAG).
- Implement retrieval-augmented generation (RAG) with vector databases like Milvus, Pinecone, Azure AI Search (with Vector Indexing).
- Optimize AI workflows using Reinforcement Learning (RLHF) and function calling for improved reasoning and automation.
- Integrate AI agents with APIs, DevOps tools and existing workflows.
- Ensure observability, debugging, and monitoring of AI agent behavior through logging frameworks.
- Implement memory persistence for long-term agent interactions using graph-based memory or event stores.
- Collaborate with cross-functional teams to embed AI agents into DevOps, automation, and software development processes.
- Implement various prompt techniques, including zero-shot, few-shot, chain-of-thought (CoT), and fine-tuning for optimized LLM performance

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