The Senior Software Engineer builds and supports software systems that enable AI-driven capabilities in products and internal platforms. This role focuses on developing reliable services, APIs, and workflows that integrate machine learning and generative AI (where applicable) into real-world applications. The engineer works closely with AI/ML engineers, data engineers, and product partners to deliver features from design through production support.

Key Responsibilities

• Develop backend services and APIs that integrate AI/ML capabilities into applications (e.g., inference endpoints, orchestration services, workflow automation).
• Implement business logic around AI outputs, including validation, confidence handling, fallback behaviors, and human-in-the-loop patterns where needed.
• Build integration layers to connect AI services with enterprise systems and data sources.

• Own delivery of highly complex features/components with limited guidance: break down tasks, estimate effort, and deliver on schedule.
• Write clean, maintainable code with appropriate abstractions and documentation.
• Build robust testing (unit/integration) and ensure features meet performance, reliability, and security expectations.

• Instrument services with logging, metrics, and tracing; support production monitoring and incident triage.
• Troubleshoot issues across environments and participate in on-call/operational support rotations as applicable.
• Apply basic responsible AI practices—logging, transparency, and guardrails—based on established team standards.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
• 5–7 years of professional software engineering experience delivering production applications/services.
• Proficiency in at least one modern language such as Python, C#,etc..
• Experience building APIs/services, working with databases, and shipping features via CI/CD pipelines.
• Solid understanding of software engineering fundamentals: data structures, debugging, testing, and code quality.
• Experience supporting AI or ML-enabled applications (e.g., integrating inference services, building orchestration around model outputs).
• Familiarity with LLM application patterns (prompt orchestration, RAG basics, tool/function calling) and evaluation concepts.

Nice to Have

• Experience with cloud platforms (e.g., Azure/AWS/GCP) and containerization (Docker/Kubernetes).
• Exposure to event-driven systems (queues/streams) and observability tooling.
• Experience with basic front-end development or building AI-assisted UX flows.

Core Skills

• Backend Engineering: REST APIs, services, async processing, microservice patterns
• Software Quality: unit/integration testing, CI/CD, code reviews, documentation
• Operations: logging/metrics/tracing, performance tuning, incident triage
• AI Integration (preferred): model inference integration, orchestration, guardrails/fallbacks
• Data Fundamentals: SQL basics, data contracts, working with structured/semi-structured data