Artificial Intelligence Engineer

Alike Thoughts; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Alike Thoughts in Bengaluru seeks an experienced Senior Python & Generative AI Cloud Engineer to design, build, and scale GenAI applications, autonomous AI agents, and Retrieval-Augmented Generation (RAG) pipelines.

You will integrate with leading LLMs, deploy on major cloud platforms, containerize with Docker, and orchestrate via Kubernetes, delivering robust RESTful APIs with FastAPI or Flask.

Qualifications

• Proven experience in Python-based GenAI projects.
• Experience integrating LLMs via APIs and orchestration frameworks.
• Strong background in building scalable API-driven services.

Responsibilities

• GenAI & LLM Integration: develop applications using OpenAI, Claude, Llama, or open-source models.
• RAG & Vector Search: design architectures with Pinecone, FAISS, or Weaviate.
• Agentic AI Workflows: build multi-agent systems and cognitive workflows.

Skills

Python

Generative AI

LLMs

Cloud computing

API development

Tools

Pinecone

FAISS

LangChain

FastAPI

Docker

Kubernetes

Job description

Job TitleSenior Python & Generative AI Cloud EngineerRole Overview

We are looking for an experienced Python Engineer with deep expertise in Generative AI, Large Language Models (LLMs), and cloud infrastructure. You will design, build, and scale production-grade GenAI applications, autonomous AI agents, and Retrieval-Augmented Generation (RAG) pipelines integrated with major cloud platforms (AWS, Azure, or GCP).

Key Responsibilities

• GenAI & LLM Integration: Develop and maintain applications leveraging OpenAI, Claude, Llama, or open-source LLMs via APIs and custom orchestration.
• RAG & Vector Search: Build advanced RAG architectures using vector databases like Pinecone, FAISS, ChromaDB, or Weaviate.
• Agentic AI Workflows: Design multi-agent systems and cognitive workflows using frameworks like LangChain, LangGraph, AutoGen, or Semantic Kernel.
• Backend & API Development: Build robust, scalable RESTful APIs and microservices using FastAPI or Flask.
• Cloud Deployment & MLOps: Deploy, monitor, and scale AI workloads using cloud-native services (AWS SageMaker, Azure AI, Google Vertex AI) packaged with Docker and orchestrated via Kubernetes.
• Performance Optimization: Optimize token consumption, model inference latency, and memory management in production.