Gen AI Lead Engineer - Python, FAST API
Fa Ewmy Saasfaprod1 Open · verified Oct 22, 2026 At a glance
Senior AI Engineering role at Fa Ewmy Saasfaprod1. Hyderabad · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Turn generative AI designs into deployed applications using LLMs and current AI frameworks
- Build and maintain Python backend services and REST APIs with FastAPI, Flask or Django
- Create RAG and semantic-search pipelines backed by vector databases, and connect LLMs to enterprise applications
- Shape prompt workflows, assess model performance and tune AI applications for speed, reliability, scale and cost
- Deploy AI applications on AWS, troubleshoot issues, and contribute to code reviews, testing and CI/CD
What you bring
- At least five years in software engineering, AI/ML or a related technical role, with strong Python programming skills
- You have built backend applications and REST APIs using FastAPI, Flask or Django
- Practical experience with generative AI or LLM applications, plus familiarity with RAG and semantic search
- You have used vector databases and connected AI models or services such as OpenAI, Anthropic or AWS Bedrock
- Prompt optimization and AI evaluation experience, with NLP exposure and familiarity with scalable architecture
Who this fits
This role suits a senior engineer focused on production AI applications, collaborating with product, architecture, data science and engineering colleagues across onshore and offshore teams. The position is hybrid in Hyderabad.
From the employer
This role is focused on building real-world GenAI solutions — not just experimentation or research. The candidate should be comfortable writing production-quality code, building APIs, integrating LLMs, and working with modern AI engineering frameworks and cloud platforms.
You will work closely with product managers, architects, data scientists, and engineering teams to develop scalable, secure, and efficient AI-powered applications.
• Design, develop, and deploy Generative AI applications using Large Language Models (LLMs) and modern AI frameworks.
• Build and maintain scalable backend services and REST APIs using Python frameworks such as FastAPI, Flask, or Django.
• Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search solutions using vector databases.
• Integrate commercial and open-source LLMs into enterprise applications.
• Develop prompt engineering workflows and support AI model evaluation and optimization.
• Deploy and support AI applications on AWS cloud platforms.
• Participate in troubleshooting and optimizing AI systems for latency, reliability, scalability, and cost efficiency.
• Collaborate with cross-functional teams across onshore and offshore locations.
• Write clean, maintainable, and testable code following software engineering best practices.
• Participate in code reviews, unit testing, integration testing, and CI/CD processes.
• Stay updated on emerging trends and technologies in Generative AI and applied machine learning.
• 5 years of experience in software engineering, AI/ML, or related technical roles.
• Strong hands-on programming skills in Python.
• Experience building backend applications and REST APIs using FastAPI, Flask, or Django.
• Practical experience working with Generative AI or LLM-based applications.
• Understanding of Retrieval-Augmented Generation (RAG) concepts and semantic search.
• Experience with vector databases such as Pinecone, OpenSearch, or similar technologies.
• Experience integrating AI models or services such as OpenAI, Anthropic, AWS Bedrock, Hugging Face, or open-source LLMs.
• Experience with AWS cloud services and deploying applications in cloud environments.
• Understanding of software engineering best practices including:
o Unit testing
o Integration testing
o Git/version control
o CI/CD workflows
• Strong debugging, analytical, and problem-solving skills.
• Good communication and collaboration skills.
Must have Qualifications
• Experience with prompt optimization and AI evaluation techniques.
• Exposure to NLP
• Understanding of design patterns and scalable application architecture.
• Experience working in agile development environments.