Engineer Jobs

About The Position

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related technical discipline.
• 10+ years of professional software engineering experience, including significant experience building and deploying LLM-powered applications.
• Proven track record of delivering enterprise-scale conversational AI or LLM-based products in production.
• Deep expertise with modern LLM APIs and models such as OpenAI GPT, Anthropic Claude, Google Gemini, Llama, or Mistral.
• Strong experience with agent frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, or AutoGen.
• Strong programming skills in Python and experience building scalable backend services and APIs.
• Experience implementing Retrieval-Augmented Generation, vector databases, embeddings, prompt engineering, and AI orchestration pipelines.
• Strong understanding of AI evaluation, model monitoring, observability, Responsible AI, and enterprise AI governance.
• Excellent communication, collaboration, analytical, and technical leadership skills.

Nice To Haves

• Public technical writing, conference presentations, open-source contributions, or thought leadership in LLM application development.
• Experience designing multi-agent architectures, autonomous AI workflows, and complex tool-use systems.
• Familiarity with LLM fine-tuning, parameter-efficient tuning (LoRA/QLoRA), and model alignment techniques.
• Experience developing AI solutions for customer support, coding assistants, enterprise search, analytics, knowledge management, or business automation.
• Experience integrating conversational AI into enterprise applications with strict compliance requirements such as HIPAA, PCI-DSS, SOC 2, or FedRAMP.
• Experience with Kubernetes, MLOps, CI/CD pipelines, cloud platforms (AWS, Azure, or GCP), and production AI infrastructure.

Responsibilities

• Design, develop, and deploy enterprise-scale conversational AI applications powered by Large Language Models.
• Define prompt engineering standards, reusable prompt libraries, and best practices for enterprise AI development.
• Architect and implement agentic AI workflows, multi-agent systems, tool orchestration, and Retrieval-Augmented Generation solutions.
• Build scalable evaluation frameworks for prompt quality, hallucination detection, response accuracy, latency, and user experience.
• Develop reusable SDKs, APIs, and developer tooling that accelerate AI application development across engineering teams.
• Collaborate with product managers, AI researchers, software engineers, and business stakeholders to deliver production-ready AI solutions.
• Implement AI safety, Responsible AI, security, compliance, guardrails, and governance practices for enterprise deployments.
• Optimize LLM performance, inference efficiency, prompt execution, caching strategies, and operational scalability.
• Mentor engineers and establish engineering standards for conversational AI architecture, testing, deployment, and monitoring.
• Evaluate emerging LLMs, agent frameworks, and AI technologies to drive innovation and continuous platform improvement.