Senior Software Engineer (AI Engineer)

Required years of experience

• 2-4 years’ experience in relevant field

Ideal candidate should have hands-on experience with at least some of the following:

• AI/LLM Integration (OpenAI API, LangChain etc.)
• API development for AI applications using Python
• Basic MLOps & Model Deployment
• Data Engineering for AI Pipelines (ETL, Pandas, NumPy, Vector databases like Pinecone/FAISS) 

What the day will look like:

• Sync with the team on priorities and progress (join daily standup meeting etc.)
• Work on AI driven development APIs, LLM prompts, and data workflow
• Improve AI workflow efficiency and data retrieval performance
• Ensure smooth deployment and scalability of AI applications
• Research and knowledge sharing
• Wrap-up and next day planning

Key Responsibilities:  

LLM Integration & AI Workflows

• Develop AI powered solutions using LLMs (GPT4, Claude, llama etc.
• Implement prompt engineering techniques (zero shot, few-shot, chain-of-thought)
• Optimize AI responses using RAG

API Development & AI Productization

• Build and optimize API to integrate AI models with applications
• Develop Python based backend services
• Implement real time and batch AI processing workflows

Data engineering and Processing

• Work with structured and unstructured data transformation
• Implement vector database storage and retrieval
• Build data pipeline

Basic MLOps & deployment

• Deploy and manage AI Models with cloud platform
• Automate model versioning, logging and monitoring
• Optimize model inference for latency and scalability

Skills/competencies required:

• 2-4 years of experience in AI, software development, data engineering or MLOps
• Strong Python skills (FastAPI, Pandas,NumPy)
• Hands on experience with AI APIs (OpenAI, Hugging Face etc.)
• Basic experience with cloud platforms for model deployment
• Understanding of data processing & vector database

How this opportunity is different –

• Work with cutting edge AI Models and API
• Opportunity to learn and grow into AI architecture role
• Direct impact on AI-powered application- real world AI automation and integration
• Cros functional learning- GenAI, data engineering, and API development