Description

Role Summary
The LLM Engineer will design, build, optimize, deploy, and operate Large Language Model and Small Language Model capabilities that power the enterprise Agent Factory. This role is responsible for transforming foundation models into secure, reliable, reusable, and enterprise-ready AI capabilities across agentic workflows, AI for SDLC, knowledge retrieval, model evaluation, private AI hosting, and AgentOps.
This engineer will work closely with the Principal AI Architect, AI Engineering Lead, Platform Engineers, Security, Enterprise Architecture, Product Owners, and domain teams to build the intelligence layer of the Agent Factory. The role requires hands-on expertise in LLM application development, RAG, prompt and context engineering, model evaluation, fine-tuning, model serving, and hosting LLMs or SLMs in cloud, hybrid, and on-prem environments.
 
Required Qualifications
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

Key Skills

Education

Any Graduate