LLM Engineer

Key Responsibilities

Experience

• 5+ years of software engineering, AI engineering, machine learning engineering, or platform engineering experience.

• 2+ years of hands-on experience building LLM, GenAI, or agentic AI solutions.

• Experience building production-grade AI applications using LLMs, RAG, prompts, APIs, and cloud or on-prem platforms.

• Experience working with model evaluation, prompt testing, and AI quality measurement.

• Experience integrating AI capabilities into enterprise applications, workflows, or developer platforms.

• Experience working in secure, governed, or regulated technology environments is preferred.

Preferred Qualifications

• Experience deploying open-source models such as Llama, Mistral, Mixtral, Phi, Gemma, Qwen, DeepSeek, Granite, Falcon, or domain-specific models.

• Experience hosting models on GPU infrastructure such as NVIDIA H100, H200, B200, B300, A100, L40S, GH200, or AMD MI300X.

• Experience with private AI, hybrid AI, or air-gapped AI environments.

• Experience with healthcare, financial services, insurance, or other regulated industries.

• Experience building enterprise copilots, AI assistants, or agent platforms.

• Experience with MCP, tool registries, agent runtimes, or enterprise integration patterns.

• Experience with Responsible AI, model governance, model risk management, and AI compliance practices.

• Experience optimizing AI workloads for cost, performance, latency, and security.

Core Deliverables

• Reusable prompt and context engineering framework.

• Enterprise RAG patterns and retrieval services.

• Model evaluation and benchmarking pipeline.

• LLM and SLM hosting patterns for cloud and on-prem environments.

• Secure inference APIs for internal agents and applications.

• Reusable model serving templates.

• Agent intelligence components for planning, reasoning, memory, and tool usage.

• AI quality dashboards for accuracy, hallucination, latency, cost, and usage.

• Model optimization playbooks.

• Responsible AI and governance controls embedded into LLM workflows.