10 часов назад
Lead AI Engineer
125 000 - 180 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Lead AI Engineer (Generative AI/LLMs): Design, build, and deploy production-ready LLM-powered solutions using transformers, retrieval-augmented generation, prompt engineering, and AI agents with an accent on reliability, scalability, safety, and measurable performance. Focus on building multi-step agentic systems, deploying and monitoring GenAI pipelines, developing evaluation frameworks, and implementing safeguards against prompt injection, unsafe tool use, and agent loops.
Location: Columbia, Maryland, United States
Starting pay range: $125,000–$180,000 per year
Company
is an AI services provider that combines data science, artificial intelligence, technology, and human expertise to deliver client solutions.
What you will do
- Architect and implement production-ready AI solutions using LLMs, transformer-based models, retrieval systems, agentic workflows, and AI agents.
- Design and optimize prompts, workflows, and RAG pipelines for accuracy, cost efficiency, latency, and safety.
- Build multi-step agentic systems that use external tools and APIs, manage state, and handle complex reasoning chains.
- Deploy and monitor GenAI models and pipelines through API, batch, or streaming architectures.
- Build evaluation frameworks for grounding, factuality, latency, and cost, and implement guardrails for privacy, safety, prompt injection, content moderation, and unsafe agent behavior.
- Collaborate with Product, Engineering, and ML Ops to deliver and maintain AI features through production.
Requirements
- 3+ years of applied machine learning experience focused on NLP, transformers, or generative AI systems.
- Hands-on experience with LLM libraries and services such as LangChain, LlamaIndex, OpenAI API, CrewAI, Azure Prompt Flow, or AWS Bedrock Agents.
- Experience designing multi-step agents with tool or API calls and safeguards against errors, loops, and unsafe tool use.
- Fluency in Python and experience building, deploying, and monitoring production-grade machine learning applications.
- Experience with cloud environments such as AWS, Azure, or GCP, including APIs, batch or streaming architectures, containerization, versioning, and CI/CD.
- Degree in Computer Science, Data Science, Engineering, or a related field, or equivalent experience; expertise in transformer-based models and LLM architectures.
Culture & Benefits
- Collaborative work at the intersection of data science and engineering.
- Medical, dental, and vision coverage.
- 401(k), paid time off, and paid holidays.
- Commuter benefits, spending accounts, life insurance, disability coverage, and employee assistance programs.
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