About the role
The Senior SDE role is for experienced developers with 5 to 7 years of expertise in building scalable applications and leading technical initiatives. This role involves solving advanced technical challenges, mentoring junior engineers, and contributing to architectural decisions.
Requirements:
- Experience: 5-7 years of experience in full-stack development, with at least 3 years in product-based companies.
- Technical Expertise: Proficient in Python with deep hands-on experience in Agentic AI frameworks, architectures, design patterns, knowledge bases, AI search, retrieval-augmented generation (RAG), context engineering, evaluation, and guardrailing.
- Agentic Thinking: Well-versed in modern AI agent patterns, including MCP, A2A, and ACP. Skilled in building modular systems where agents collaborate, reason, and adapt dynamically to context.
- MLOps / LLMOps Expertise: Experienced in model lifecycle management, prompt versioning, fine-tuning, context management, agent monitoring, and AI experimentation using platforms such as Azure AI Foundry, Hugging Face, and LangChain.
- Startup Mindset: Self-starter with a proactive, resourceful, and curious approach; comfortable working in fast-paced environments.
- Collaboration and Communication: Strong communicator and documentation advocate, committed to knowledge sharing and building scalable solutions.
- Education: Bachelor's degree in Computer Science, Engineering, or a related field.
- Technical Leadership: Drive the design and implementation of AI agent systems, ensuring scalability, reliability, and adherence to best practices.
- Innovation in Agentic AI: Develop and optimize agent-based architectures leveraging MCP, A2A, and ACP patterns for dynamic, context-aware solutions.
- MLOps / LLMOps Management: Oversee model lifecycle processes, including fine-tuning, prompt management, and deployment pipelines, ensuring robust monitoring and governance.
- Cross-Functional Collaboration: Partner with product, engineering, and operations teams to align AI solutions with business objectives.
- Problem-Solving: Address complex AI challenges, including retrieval-augmented generation, context engineering, and guardrailing, to deliver safe and effective solutions.
- Agile Practices: Contribute to Agile workflows, including sprint planning, backlog refinement, and iterative delivery of AI features.
Technology Stack:
- Backend: C#, ASP. NET, MongoDB, Postgres, EventStore.
- Frontend: React, TypeScript.
- Cloud Services: AWS SQS, AWS S3 AWS EKS.
- Messaging and Integration: NServiceBus.
- Architecture Patterns: Domain-Driven Design (DDD), Command Query Responsibility Segregation (CQRS), Event Sourcing, and Clean Architecture.