Senior Software Engineer, AI Operations, GPS
Salary not listed • Doha, Qatar • Full-time
Posted 2mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Remote Work Policy
On-site
Employment Type
Full-time
Categories
LLM Engineer
About the job
As a Senior Software Engineer, AI Ops, you will be responsible for the long-term technical health, performance, and stability of AI solutions deployed for strategic public sector partners. You will act as the technical steward, ensuring operational excellence by bridging software engineering, MLOps, and client governance. This includes managing service level agreements (SLAs), tracking model drift, and executing maintenance protocols to protect system integrity and operational margins.
Responsibilities
- Act as the technical gatekeeper for the transition from delivery to maintenance, reviewing code, prompts, and architecture for maintainability and documentation.
- Own technical response and resolution for multi-tiered SLAs, leading incident governance, root cause analysis, and critical incident mitigations.
- Monitor production model performance, latency, and data drift, and manage prompt configurations for consistency.
- Classify incoming client requests to distinguish between routine maintenance and system evolution, and benchmark new AI models.
- Engineer automated solutions for operational tasks, including self-healing data pipelines, RAG indexing syncs, and telemetry tooling.
- Serve as the senior technical point of contact for clients, translating AI concepts into business impacts for non-technical stakeholders.
Requirements
- 5+ years of experience in Software Engineering, MLOps, SRE, or Forward Deployed Engineering in data-intensive or production AI environments.
- Advanced proficiency in Python, SQL, REST/gRPC APIs, and cloud architecture (AWS, Azure, or GCP).
- Hands-on experience with MLOps tooling, vector databases, and LLM orchestration frameworks (e.g., LangChain, LlamaIndex).
- Practical understanding of prompt version control, model benchmarking, RAG pipeline mechanics, and data drift detection.
- A strong engineering mindset focused on building systematic, automated fixes.
- Solid grasp of CI/CD for machine learning pipelines.
- Strong technical communication skills for managing client expectations and advising on system roadmaps.