AI Engineer

AI systems

• Build, fine-tune, and evaluate LLM systems for domain-specific tasks (QLoRA / PEFT on open-weight models such as Llama-3 and Mistral).

• Design reproducible evaluation harnesses and A/B test frameworks with tracked metrics: task success rate, safety rate, and latency distributions (p50/p95).

• Architect multi-agent and RAG systems (LangGraph, FastAPI, vector databases) from prototype through production.

• Implement safety guardrails — input/output validation, allowlist/denylist policies, and controls that reduce invalid or high-risk model actions.

• Translate business use cases into deployable prototypes with measurable acceptance criteria, and demo them to stakeholders.

Platform & infrastructure

• Design and operate cloud infrastructure and MLOps workspaces (Azure, OCI, or GCP) for AI workloads on Kubernetes and containerized runtimes.

• Build CI/CD pipelines and GitOps-based release promotion (Argo CD) across development, test, and production environments.

• Implement end-to-end observability (Azure Monitor, Application Insights, ELK) with defined detection and response targets.

• Apply network and perimeter security baselines (FW/WAF), automated code quality and SCA scanning (SonarQube, Black Duck), and gated pipelines.

• Own disaster recovery design — automated backups, failover, and documented RTO/RPO commitments.

Engineering leadership

• Lead and mentor a cloud/AI operations team; define monitoring, incident response, and release governance practices with clear uptime and MTTR targets.

• Standardize SDLC practices — branching strategy, PR governance, release management, delivery reporting — to improve lead time and deployment frequency.

• Consolidate engineering tooling and workflows; drive migrations and platform standardization where fragmentation slows delivery.

• Produce handover documentation and runbooks that make systems auditable and operationally transferable.

• Support vendor and licensing negotiations for cloud enterprise agreements.