Senior Software Engineer

What you'll do

• Design, develop, and ship production-grade AI applications including conversational assistants, RAG pipelines, and multi-agent systems
• Architect scalable, secure, and cost-efficient backend services using Python, Node.js, and cloud-native patterns
• Build and maintain API services integrating AI capabilities with enterprise systems
• Write clean, testable, well-documented code with CI/CD standards
• Build and optimize LLM-powered features including prompt engineering and context management
• Design and implement evaluation frameworks for AI outputs ensuring trust and accuracy
• Work hands-on with LLM APIs and make informed decisions on model selection and strategies
• Design and build enterprise RAG pipelines and manage vector databases
• Continuously improve retrieval quality with testing and feedback loops
• Design and implement agentic workflows and multi-agent orchestration frameworks
• Develop reusable tool integrations with safety controls
• Work with structured and unstructured enterprise data for AI consumption
• Apply data science fundamentals to diagnose issues and validate model behavior
• Collaborate with data engineering teams for reliable data pipelines
• Implement Responsible AI practices including hallucination handling and PII protection
• Build and operate LLMOps / MLOps pipelines for model deployment and monitoring
• Contribute to governance documentation and operational runbooks

Requirements

• 5–8+ years in software engineering
• At least 2+ years hands-on in Generative AI / LLM-based systems
• Strong proficiency in Python
• Experience with backend frameworks (FastAPI, Flask, Express/Node.js)
• Clean API design, version control (Git), testing, and CI/CD
• Hands-on experience with LLM APIs (Azure OpenAI, AWS Bedrock, Anthropic, Google Gemini)
• Experience in prompt engineering, structured outputs, tool/function calling
• Proven experience building RAG pipelines including embedding models, chunking, retrieval logic, vector database, re-ranking, and grounding
• Experience designing agent-based architectures with multi-step workflows
• Solid understanding of data wrangling, SQL, EDA, and basic ML concepts
• Experience with Databricks Lakehouse Platform
• Experience with AWS or Azure cloud services
• Ability to design distributed, scalable AI systems

Tech stack

Benefits