Senior Software Engineer
On-siteBengaluru, Karnataka, India
Job Summary
Design, develop, and ship production-grade AI applications including conversational assistants, RAG pipelines, and multi-agent systems. Architect scalable backend services using Python, Node.js, and cloud-native patterns on AWS, Azure, or GCP. Build and optimize LLM-powered features with prompt engineering, structured outputs, and tool/function calling while implementing evaluation frameworks for accuracy and quality. Design enterprise RAG pipelines with embedding selection, hybrid retrieval, and vector databases to manage knowledge retrieval across heterogeneous data sources. Implement agentic workflows for autonomous reasoning, planning, and multi-step business processes with human-in-the-loop checkpoints. Operate LLMOps/MLOps pipelines for model deployment, monitoring, logging, and lifecycle management while enforcing responsible AI practices and security guardrails.
Required Qualifications
- 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
- CI/CD
- Hands-on experience with LLM APIs (Azure OpenAI, AWS Bedrock, Anthropic, Google Gemini)
- prompt engineering
- structured outputs
- tool/function calling
- Proven experience building RAG pipelines
- embedding models
- chunking
- retrieval logic
- vector database
- re-ranking
- grounding
- Experience designing agent-based architectures
- tool use
- planning
- multi-step workflows
- familiarity with AWS Bedrock, Azure AI Foundry, or equivalent frameworks
- Experience with AWS or Azure
- deploying containerized services
- serverless functions
- working with cloud AI/ML services
- Ability to design distributed, scalable AI systems
- clear tradeoffs on cost, latency, and reliability
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent practical experience)
Desired Qualifications
- Multi-Agent Systems & A2A Protocols
- experience with agent-to-agent communication patterns
- Model Context Protocol (MCP)
- similar emerging standards
- Fine-Tuning & Model Adaptation
- experience fine-tuning LLMs
- adapter-based methods (LoRA, QLoRA)
- domain-specific use cases
- AI Evaluation & Benchmarking
- experience building evaluation harnesses
- automated grading
- regression testing for LLM outputs
- Microsoft Ecosystem
- familiarity with M365 Copilot
- Copilot Studio
- Bot Framework
- Teams integrations
- Adaptive Cards
- Observability & Tracing
- experience with AI-specific observability
- debugging and monitoring AI systems in production
- NLP & Classical ML
- deeper grounding in NLP
- named entity recognition
- text classification
- sentiment analysis
- classical ML
- scikit-learn
- XGBoost
- Knowledge Graphs & Hybrid Search
- experience combining graph-based retrieval
- vector search
- richer contextual grounding
- Edge / Cost Optimization
- techniques for reducing inference cost
- model distillation
- quantization
- caching
- batching strategies
- Security & Compliance
- awareness of data privacy regulations
- secure API design
- AI red-teaming / adversarial testing
- You think like a software engineer first
- you care about clean code
- testable systems
- operational excellence
- You have a builder's approach
- you're not just consuming APIs
- you're designing platforms
- building reusable components
- thinking about how your work scales to 50+ teams
- You bring data intuition
- you can EDA your way through a problem
- validate model behavior with data
- explain tradeoffs with metrics
- You're curious and forward-looking
- you track the evolving landscape of AI agents
- evaluation
- orchestration
- bring those ideas to the team
- You thrive in a fast-paced, collaborative environment
- work closely with product, operations, and leadership
- deliver measurable impact
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