Software Engineering Lead

Role & responsibilities

• Architect, design, and deliver scalable, secure, cloud-native AI platforms
• Lead development of Generative AI and Agentic AI solutions, including multi-agent systems
• Own end-to-end AI service lifecycle: design, development, deployment, performance tuning, and production support
• Perform deep performance profiling, optimization, and reliability improvements for backend AI systems
• Collaborate with product, architecture, security, and governance teams to ensure compliant and responsible AI
• Mentor junior engineers; provide technical guidance and design reviews
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Preferred candidate profile

• Graduate degree or equivalent experience
• B.Tech / M.Tech in Computer Science or AI or relevant field
• 6+ years of experience working on Machine learning and Agentic AI solutions building towards Process and Tasks Automation for complex business processes
• Solid hands-on experience with FastAPI, including high-performance asynchronous implementations
• Proven experience designing large-scale backend systems and APIs
• Extensive hands-on experience with GCP or Azure, or AWS
• Experience deploying and operating mission-critical AI workloads in production
• Deep experience with vector databases (e.g., Redis, ChromaDB, or equivalent)
• Expert-level experience with Docker
• Solid experience with performance testing, stress testing, and system optimization
• Solid working knowledge of Kubernetes, including scaling, resiliency, and troubleshooting
• Solid understanding of cloud architecture, networking, IAM, security, and cost optimization
• Expert-level proficiency in Python
• Proven solid command of SQL and NoSQL databases, indexing strategies, and performance tuning
• LLMs and agentic AI architectures
• Multi-agent frameworks
• Google ADK, Anthropic MCP, or similar platforms
• Proven advanced skills in prompt engineering, evaluation frameworks, and AI observability
• Proven ability to identify architectural bottlenecks and propose scalable solutions