AI Engineer
Description
AI systems
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Build, fine-tune, and evaluate LLM systems for domain-specific tasks (QLoRA / PEFT on open-weight models such as Llama-3 and Mistral).
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Design reproducible evaluation harnesses and A/B test frameworks with tracked metrics: task success rate, safety rate, and latency distributions (p50/p95).
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Architect multi-agent and RAG systems (LangGraph, FastAPI, vector databases) from prototype through production.
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Implement safety guardrails — input/output validation, allowlist/denylist policies, and controls that reduce invalid or high-risk model actions.
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Translate business use cases into deployable prototypes with measurable acceptance criteria, and demo them to stakeholders.
Platform & infrastructure
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Design and operate cloud infrastructure and MLOps workspaces (Azure, OCI, or GCP) for AI workloads on Kubernetes and containerized runtimes.
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Build CI/CD pipelines and GitOps-based release promotion (Argo CD) across development, test, and production environments.
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Implement end-to-end observability (Azure Monitor, Application Insights, ELK) with defined detection and response targets.
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Apply network and perimeter security baselines (FW/WAF), automated code quality and SCA scanning (SonarQube, Black Duck), and gated pipelines.
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Own disaster recovery design — automated backups, failover, and documented RTO/RPO commitments.
Engineering leadership
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Lead and mentor a cloud/AI operations team; define monitoring, incident response, and release governance practices with clear uptime and MTTR targets.
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Standardize SDLC practices — branching strategy, PR governance, release management, delivery reporting — to improve lead time and deployment frequency.
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Consolidate engineering tooling and workflows; drive migrations and platform standardization where fragmentation slows delivery.
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Produce handover documentation and runbooks that make systems auditable and operationally transferable.
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Support vendor and licensing negotiations for cloud enterprise agreements.