Lead Software Developer - ANJU

Aspire Software

The Technical Engineering Manager leads a cross-functional team (Development & QA) and owns technical architecture, delivery outcomes, and AI-enabled system design.

This role is both strategic and hands-on. It requires strong expertise in modern AI frameworks, applied AI architectures, and scalable system design. The manager is accountable for delivering stable, secure, AI-powered capabilities not just managing execution.

Technical Leadership & Architecture

• Own product architecture, including AI/LLM-integrated components
• Design scalable, secure, and maintainable systems
  
  Lead decisions involving:
• LLM integration (OpenAI, Azure OpenAI, etc.)
• RAG architectures and vector databases
• AI orchestration frameworks (LangChain, Semantic Kernel, LlamaIndex)
• Ensure system reliability, observability, and cost efficiency
• Establish AI governance and safe usage practices

AI Framework & Methodology Expertise

Apply modern AI methodologies, including:

• Retrieval-Augmented Generation (RAG)
• Prompt engineering and evaluation
• AI output validation and guardrails
• Human-in-the-loop workflows
• Model monitoring and performance evaluation
• Drive experimentation, A/B testing, and telemetry-based decision making

Team Leadership

• Lead and mentor developers and QA engineers
• Raise AI literacy across the team
• Establish AI-aware coding and review standards
• Balance speed, quality, and architectural integrity

Delivery & Outcome Ownership

• Own predictable, high-quality releases
• Ensure AI features are measurable, validated, and production-ready
• Act as technical escalation point
• Drive cross-team alignment with Product, Data, and DevOps

AI-Driven Engineering Excellence

• Leverage AI-assisted development tools to improve velocity
• Optimize AI cost-performance tradeoffs
• Embed automation into testing and CI/CD pipelines

• 7+ years software engineering experience, 2+ years in technical leadership
• Hands-on experience building and deploying AI-enabled systems
  
  Strong knowledge of:
• LLM integration and prompt design
• RAG architectures and vector search
• AI orchestration frameworks
• Cloud platforms (Azure, AWS, or GCP)
• Experience designing scalable distributed systems