About the Role
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As a Senior Software Engineer focusing on AI Ops, you will be responsible for the long-term technical health, performance, and stability of AI solutions deployed across strategic public sector partners. Your role is crucial as you ensure these deployments operate with operational excellence while managing technical aspects of software engineering, MLOps, and client governance. This includes overseeing tiered service level agreements (SLAs), tracking model drift, and executing maintenance protocols that safeguard both system integrity and operational margins.
Key Responsibilities
- Handover Gate & Onboarding: Act as the technical gatekeeper during the transition from delivery to maintenance, conducting deep-dive reviews to guarantee that baseline code, prompts, and architecture meet maintainability and documentation standards before sign-off.
- Tiered SLA & Incident Management: Own technical response and resolution targets across various service models, leading incident governance, root cause analysis (RCA), and mitigations within strict support windows.
- AI Lifecycle Governance: Monitor production model performance, latency, and data drift while managing prompt configuration repositories to maintain behavioral consistency and perform regression testing when LLM providers update underlying endpoints.
- Request Classification & Technical Scope: Distinguish between routine maintenance and system evolution, assessing client requests and benchmarking new AI models.
- Automation & Reliability Engineering: Eliminate operational toil through engineering of self-healing data pipelines, automated processes, and telemetry tooling, while influencing upstream teams to adopt maintenance-friendly architectural patterns.
- Client Technical Interface: Serve as the senior technical point of contact for government and enterprise IT leads, translating complex AI concepts into clear business impacts for non-technical stakeholders.
Ideally you'd have
- Background: 5+ years in Software Engineering, MLOps, SRE, or Forward Deployed Engineering in heavy data or production AI environments.
- Technical Stack: 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).
- AI Governance Expertise: Practical understanding of prompt version control, model benchmarking, RAG pipeline mechanics, and data drift detection.
- Engineering Mindset: A strong inclination towards building systematic, automated solutions rather than temporary fixes, with a solid grasp of CI/CD processes for machine learning pipelines.
- Client Acumen & Boundary Control: Excellent technical communication skills, ability to manage client expectations effectively, defend operational boundaries, and advise on long-term system roadmaps.
Contract and Travel Details
- For those applying based in Qatar: Residency and employment in Qatar require certain permissions (visa and permits) issued by Qatari authorities. Candidates will need to provide personal information such as residency status and nationality for the visa process. Visa issuance is at the discretion of the Qatari authorities. Successful candidates will be supported in obtaining the necessary permissions to live and work in Qatar.
Note: There is a 90-day waiting period before considering candidates for the same role again, ensuring a fair evaluation process for all applicants.
