Ai Ml Engineer

SIRO Clinpharm Pvt. Ltd.; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

SIRO Clinpharm Pvt. Ltd. is seeking a GenAI Application Developer (Python) with 7-10 years of experience to design and deploy enterprise GenAI applications. The role focuses on Python, FastAPI, LLMs, RAG, vector databases, and AWS cloud, delivering scalable APIs and microservices.

You will integrate LLMs across OpenAI, Azure OpenAI, AWS Bedrock, or Vertex AI and drive cloud-native deployment with containerized architectures, collaborating with cross-functional teams.

Qualifications

• Strong expertise in Python, FastAPI, LLMs, RAG and vector databases.
• Experience designing enterprise GenAI apps on cloud platforms (AWS, Azure, GCP).
• Ability to build scalable REST APIs and microservices.

Responsibilities

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

Skills

Python

FastAPI

LLMs

RAG

Vector Databases

AWS Cloud

REST APIs

Microservices

CI/CD

Tools

Docker

Kubernetes

ECS

EKS

OpenAI

Azure OpenAI

AWS Bedrock

Vertex AI

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.