8 days ago
Generative AI Engineer
Keystone Recruitment
Hybrid
Contractor
$200,000
Hybrid
Job Overview
Job TitleGenerative AI Engineer
Job TypeContractor
CategoryCommerce
Experience5 Years
DegreeMaster
Offered Salary$200,000
LocationHybrid
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Job Description
About the Role
One of our enterprise AI clients is seeking a highly skilled and motivated Generative AI Engineer to design, develop, and deploy advanced AI solutions leveraging large language models, multimodal transformers, and generative architectures. This is a senior-level, high-impact role focused on building production-grade AI systems across content generation, conversational AI, autonomous agents, and intelligent data synthesis use cases.
Key Responsibilities
- Develop and fine-tune large language models (such as GPT-class, LLaMA-family, Mistral-family, Claude-class models) for domain-specific downstream tasks
- Design and optimize Retrieval-Augmented Generation (RAG) pipelines using frameworks like LangChain, LlamaIndex, or Haystack
- Build end-to-end generative AI applications spanning text, code, image, and audio use cases
- Implement embedding-based retrieval systems using vector databases such as FAISS, Pinecone, Weaviate, or Qdrant
- Integrate foundation model APIs into scalable production workflows
- Work with multimodal models and generative systems for image, vision-language, and audio tasks
- Optimize model latency, throughput, and scalability for production environments
- Collaborate cross-functionally with ML engineers, data teams, and product stakeholders
- Stay current with advancements in generative AI research and apply them to real-world systems
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
- Strong hands-on experience in ML/AI engineering, with demonstrated experience in generative AI or LLM-based systems
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Experience using HuggingFace Transformers, LangChain, or similar ecosystems
- Strong understanding of NLP, transformer architectures, embeddings, and deep learning fundamentals
- Experience building scalable ML pipelines and deploying models using Docker, Kubernetes, or similar infrastructure tools
- Familiarity with prompt engineering, fine-tuning strategies, evaluation methodologies, and production model monitoring
Key skills/competency
- Generative AI
- Large Language Models (LLMs)
- RAG pipelines
- Python
- PyTorch
- TensorFlow
- HuggingFace Transformers
- NLP
- Vector Databases
- ML Pipelines
How to Get Hired at Keystone Recruitment
- Research Keystone Recruitment's clients: Study their mission, values, recent AI projects, and employee testimonials on LinkedIn and Glassdoor to understand their focus areas.
- Tailor your resume meticulously: Customize your resume and cover letter to highlight experience with generative AI, LLMs, RAG, and relevant frameworks like LangChain, aligning with the Generative AI Engineer job description.
- Showcase your Generative AI portfolio: Prepare a strong portfolio or GitHub profile demonstrating practical projects involving LLM development, multimodal models, and scalable AI system deployment.
- Ace the technical interview: Be ready for in-depth questions on transformer architectures, vector databases, prompt engineering, and ML pipeline optimization specific to generative AI.
- Emphasize collaboration and innovation: During behavioral interviews, highlight your ability to collaborate with cross-functional teams and stay current with cutting-edge AI research and application.
Frequently Asked Questions
Find answers to common questions about this job opportunity
01What specific large language models (LLMs) are central to the Generative AI Engineer role at Keystone Recruitment's client?
02How critical is experience with Retrieval-Augmented Generation (RAG) pipelines for this Generative AI Engineer position?
03What is the typical interview process for a Generative AI Engineer role placed by Keystone Recruitment?
04Can you elaborate on the 'multimodal models' experience required for this Generative AI Engineer role?
05What infrastructure tools are used for deploying Generative AI models in this role?
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