GenAi Specialist
At a glance
Mid-level AI Engineering role at Hudson Manpower. Maryland, Maryland, United States · full-time · no salary stated · we estimate $132,450–$205,151 across the United States.
Hudson Manpower has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and adapt large language models, GANs and other generative systems for specific applications
- Apply transfer learning, reinforcement learning and multimodal methods, then shape architectures for efficient, scalable performance
- Integrate GenAI into applications with other teams and maintain the data preparation pipelines that support training
- Run experiments to check model quality, make improvements, and record methods and findings for stakeholders
- Address bias in data and model outputs, follow ethical AI standards, and mentor teammates on GenAI projects
What you bring
- At least three years creating generative AI applications or working with large-scale machine learning models
- A demonstrated history of deploying AI models in production
- Strong Python ability and proficiency with machine learning frameworks such as TensorFlow, PyTorch or another listed tool
- Expertise in NLP, transformers and multimodal AI, plus work with large datasets and distributed computing
Who this fits
This role suits a mid-level GenAI specialist who can develop and deploy models and provide technical guidance to teammates. The job is onsite in Abu Dhabi or Maryland, USA.
From the employer
Hello,
I hope this message finds you well.
I’m reaching out to share an exciting opportunity that I believe aligns with your experience and expertise in the field for my client. If you are interested, I’d love to connect and share more details.
GenAI Specialist - Government client(Confidential)
Location : Abu dhabi OR Maryland, USA
Rate : 33K AED/Month OR $125K P.A Max
Job Description :
We are seeking a highly skilled GenAI Specialist to lead the development and deployment of cutting-edge generative AI models. This role is pivotal in advancing our AI capabilities, leveraging state-of-the-art techniques in natural language processing (NLP), computer vision, and generative modeling to solve complex problems. The ideal candidate will bring technical expertise, creativity, and a passion for innovation to drive impactful projects.
Key Responsibilities
Develop and fine-tune generative AI models, such as large language models (LLMs) and GANs, for specific use cases.
Research and implement advanced techniques in AI, including transfer learning, reinforcement learning, and multimodal AI.
Design and optimize model architectures to enhance performance, scalability, and efficiency.
Collaborate with cross-functional teams to integrate GenAI solutions into applications and systems.
Build and maintain pipelines for data collection, preprocessing, and annotation to support model training.
Conduct experiments to evaluate model performance and implement continuous improvements.
Stay up-to-date with advancements in GenAI research and tools, incorporating them into projects as appropriate.
Ensure compliance with ethical AI standards and address bias in datasets and model predictions.
Create detailed documentation of methodologies, findings, and best practices for internal and external stakeholders.
Provide mentorship and technical guidance to team members working on GenAI projects.
Qualifications
Education:
Education qualifications listed below will be considered but are not required.
Master’s or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related field.
Experience:
3+ years of experience in developing generative AI solutions or working with large-scale machine learning models.
Proven track record of deploying AI models in production environments.
Technical Skills:
Proficiency in machine learning frameworks and tools (e.g., TensorFlow, PyTorch, Hugging Face).
Expertise in natural language processing (NLP), transformers, and multimodal AI.
Experience with large-scale datasets and distributed computing frameworks (e.g., Spark, Dask).
Strong programming skills in Python and familiarity with libraries like NumPy, pandas, and Scikit-learn.
Knowledge of cloud platforms and AI infrastructure (e.g., AWS Sagemaker, Google Cloud AI).
Familiarity with MLOps practices for model deployment, monitoring, and management.