ob Title: Generative AI Engineer (Python &

- Cloud )Job OverviewWe are seeking a highly skilled Generative AI Engineer to design, build, and deploy production-grade Artificial Intelligence solutions. In this role, you will bridge the gap between advanced machine learning research and robust cloud software engineering. You will build scalable pipelines, optimize Large Language Models (LLMs), and deploy secure Retrieval-Augmented Generation (RAG) applications using Python and cloud ecosystems (AWS or Azure). [1, 2, 3, 4, 5]Core Responsibilities

- AI Application Development: Design and implement generative workflows, autonomous agents, and RAG architectures using frameworks like LangChain, LlamaIndex, or AutoGen. [1, 2, 3]

- LLM Engineering &
- Fine-Tuning

: Optimize foundational models through prompt engineering, parameter-efficient fine-tuning (PEFT/LoRA), and model quantization. [1]

- Cloud Infrastructure &

- Pipelines

: Architect secure, auto-scaling AI microservices on cloud platforms using containerized environments. [1]

- Data &

- Vector Management

: Build high-throughput data ingestion pipelines into vector databases for semantic search and real-time knowledge retrieval.

- API Integration: Create and maintain clean,



well-documented RESTful APIs to expose generative capabilities to front-end systems. [1, 2]

- MLOps &

- Monitoring

: Set up continuous integration and deployment (CI/CD) paths, model tracking, latency auditing, and token cost governance. [1, 2]

Technical Skills Requirements1. Core Programming &

- AI Libraries

- Language: Expert proficiency in Python (async programming, type hinting, and design patterns).

- AI Frameworks: Hands-on experience with Hugging Face Transformers, LangChain, LlamaIndex, or Guidance.

- API Development: Robust mastery of FastAPI, Flask, or modern API orchestration layers. [1]

2. Cloud &

- MLOps Infrastructure (AWS or Azure)

- If AWS Stack: Deep familiarity with Amazon Bedrock, SageMaker JumpStart, AWS Lambda, Amazon ECS/EKS, and AWS IAM policies.

- If Azure Stack: Deep familiarity with Azure OpenAI Service, Azure Machine Learning, Azure Functions, Azure Kubernetes Service (AKS), and Azure Entra ID.

- Vector Databases: Experience with Pinecone, Milvus, Qdrant, PGVector, or cloud-native vector indexes (e.g., OpenSearch, Azure AI Search).

- DevOps / IaC: Version control using Git, automation through GitHub Actions or GitLab CI, and infrastructure management via Terraform.Role & responsibilities

📌 Software Engineer (Bengaluru)
🏢 Alike Thoughts
📍 Bengaluru