Contribute to the design and implementation of state-of-the-art AI solutions.
Assist in the development and implementation of AI models, and deploy advanced generative AI models for text, vision, audio, or multimodal applications.
Fine-tune and customize LLMs and generative models for domain-specific tasks, leveraging RLHF, transfer learning, and continual learning approaches.
Collaborate with stakeholders to identify business opportunities and define AI project goals.
Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases.
Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.
Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related field
Minimum 4 years of experience in Python, Data Science, Machine Learning, OCR and document intelligence
Strong foundation in machine learning and data science: statistical analysis, feature engineering, modelling (supervised/unsupervised), NLP, optimization techniques.
Hands-on experience with generative modelling (GANs, VAEs, diffusion models) and large language models (LLMs).
Familiarity with agentic AI concepts: designing agents that plan, act, interact, have memory/context, and orchestrate workflows.
Proficiency in Python and relevant ML/AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face).
Ability to define and build data pipelines, work with structured/unstructured data, and apply data science best practices.
Excellent problem-solving skills and the ability to work in a fast-moving, innovative environment.
Strong communication and collaboration skills: able to interface with engineering, data science, product and business teams.
Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models.
Familiarity with computer vision techniques for image recognition, object detection, or image generation.
Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

PythonData ScienceMachine LearningOCRGenerative ModellingLarge Language ModelsStatistical AnalysisFeature EngineeringNLPOptimization Techniques

Problem-solvingCommunicationCollaboration

Bachelor's degreeMaster's degree