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Senior AI Engineer
Skills
Amazon Web ServicesConsumer TextilesContextDatabasesDomain Name SystemIdentity And Access ManagementTransmission Control Protocol
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 7+ years of software data or infrastructure engineering experience
- 3+ years working with modern AI or LLM systems
- Production-quality Python with testing code review and version control fluency
- Deep Linux engineering and performance troubleshooting experience
- Deep Docker experience including image build registry management and security
- Strong server hardware fundamentals including CPU and GPU topologies PCIe BMC and BIOS firmware lifecycle
- Hands-on experience with HPE PCAI Dell AI Factory or Nutanix Enterprise AI
- Production experience deploying tuning and operating vLLM
- Working knowledge of multiple inference and model-serving frameworks beyond vLLM
- Hands-on experience with high-throughput low-latency storage and network fabrics for AI workloads
- Practical experience with MLOps tooling including registries deployment pipelines GitOps lineage and rollback
- Hands-on experience deploying tuning and integrating vector databases and RAG pipelines
- Production experience designing system prompts structured output function calling and tool-using LLM patterns
- Demonstrated experience designing LLM evaluation harnesses with golden sets regression suites and metrics
- Strong client-facing communication and stakeholder management skills
- Track record of mentoring more junior engineers and improving team quality
- Networking fundamentals including TCP/IP DNS load balancing VLANs and firewall administration
- Comfort working across multiple concurrent client environments and competing priorities under SLA
Nice to have
- Curious about end-to-end AI systems
- Calm under production pressure
- Client-oriented
- Ambiguous environment comfort
- Service mindset
- Practice builder
Day to day
- Design, build, and operate enterprise AI systems across multiple client engagements using AI Factory platforms and the broader AI stack.
- Engineer and tune LLM inference and RAG solutions for latency throughput cost and quality targets while managing observability and reliability.
- Lead technical troubleshooting mentoring client communication and knowledge transfer to deliver production AI outcomes and reusable reference designs.