Ai Ml Engineer

SIRO Clinpharm Pvt. Ltd.; India; Remote; INR 1,500,000 - 2,400,000; Full time

Job summary

SIRO Clinpharm Pvt. Ltd. invites an experienced GenAI Application Developer (Python) to design, develop, and deploy enterprise-grade Generative AI applications.

You will work with Python, FastAPI, LLMs, RAG, and vector databases to build scalable AI solutions that integrate with cloud services. You will create REST APIs, containerized deployments with Docker and Kubernetes, and collaborate with architects and product teams to deliver robust AI capabilities across platforms such as OpenAI, Azure

Qualifications

• 7–10 years of experience in GenAI application development.
• Proven expertise in Python, FastAPI, LLMs, RAG, and vector databases.
• Experience deploying AI-powered services on cloud platforms (AWS, Azure, GCP).

Responsibilities

• Design and develop GenAI applications using Python and FastAPI.
• Integrate LLMs via OpenAI, Azure OpenAI, AWS Bedrock, or Vertex AI.
• Build scalable REST APIs and microservices for AI-powered apps.
• Deploy containerized apps with Docker and Kubernetes (ECS/EKS).
• Develop and optimize vector database solutions and enterprise search.

Skills

Python

FastAPI

LLMs

RAG

Vector databases

AWS Cloud

Tools

Docker

Kubernetes

ECS

EKS

OpenAI

Job description

Greetings!

Hiring for

Job Title: GenAI Application Developer (Python)

Experience: 7-10 Years

Job Description:

We are seeking a GenAI Application Developer (Python) with strong expertise in Python, FastAPI, Large Language Models (LLMs), RAG, Vector Databases, and AWS Cloud. The candidate will design, develop, and deploy enterprise-grade Generative AI applications, integrating LLMs with scalable backend services and cloud-native architectures.

Key Responsibilities:

• Design and develop enterprise GenAI applications using Python and FastAPI.
• Integrate LLMs through OpenAI, Azure OpenAI, AWS Bedrock, or Vertex AI.
• Develop RAG solutions including document ingestion, chunking, embedding, indexing, and semantic retrieval.
• Build scalable REST APIs and microservices for AI-powered applications.
• Deploy containerized applications using Docker, Kubernetes, ECS, and EKS.
• Develop and optimize vector database solutions and enterprise search capabilities.
• Implement CI/CD pipelines and cloud-native deployment strategies.
• Collaborate with architects, product owners, and engineering teams to deliver AI solutions.