977 Ventures

Generative AI Engineer

977 Ventures

Generative AI Engineer

Generative AI Engineer

977 Ventures

Kathmandu, Bāgmatī, Nepal
Experience: More than 3 years
Key Skills: Generative Ai Communication Transfer Learning Large Language Models (Llm) Natural Language Processing (Nlp)

Generative AI Engineer

Views: 361 | This job is expired 8 months ago

Basic Job Information

Job Category : IT & Telecommunication
Job Level : Mid Level
No. of Vacancy/s : [ 1 ]
Employment Type : Contract
Job Location : Kathmandu, Bāgmatī, Nepal
Apply Before(Deadline) : Nov. 03, 2024 23:55 (8 months ago)

Job Specification

Education Level : Under Graduate (Bachelor)
Experience Required : More than 3 years
Professional Skill Required : Generative Ai Communication Transfer Learning Large Language Models (Llm) Natural Language Processing (Nlp)

About the job

Compensation: DOE

Role Overview

We are seeking a skilled Generative AI Engineer with a strong foundation in traditional Natural Language Processing (NLP) techniques and deep experience in building and deploying large language models (LLMs). This role requires a balance of expertise in state-of-the-art generative AI and traditional machine learning (ML) methods to drive innovative AI solutions.

Responsibilities

Model Development:

  • Develop and fine-tune LLMs using frameworks such as OpenAI, Hugging Face, or Google JAX.
  • Build and improve text generation, summarization, question answering, and dialogue generation models.
  • Utilize traditional NLP techniques, including tokenization, word embeddings, and feature engineering for enhanced model performance.

Research & Experimentation:

  • Stay updated with the latest advancements in generative AI, NLP, and ML, applying this knowledge to create scalable, production-ready models.
  • Conduct experiments to assess and benchmark model performance on tasks such as sentiment analysis, text classification, entity recognition, and language understanding.

Model Deployment & Optimization:

  • Implement and optimize LLMs and NLP models in production, using best practices for model serving, scaling, and API integration.
  • Work with tools such as TensorFlow Serving, TorchServe, and containerized environments (Docker, Kubernetes) for efficient model deployment.

Data Collection & Preparation:

  • Design pipelines for data preprocessing, augmentation, and transformation.
  • Collaborate with data engineering teams to ensure the availability and quality of datasets for model training.

Collaboration & Communication:

  • Collaborate with product managers, data scientists, and other engineering teams to translate business requirements into technical implementations.
  • Communicate technical concepts effectively to both technical and non-technical stakeholders.

Requirements

Education:

  • Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or a related field.

Experience:

  • 3-5 years of hands-on experience in developing and deploying NLP models, including experience with at least one modern LLM platform (e.g., GPT, BERT, T5, LLaMA).
  • Demonstrated expertise in traditional NLP approaches and techniques (e.g., TF-IDF, CRF, POS tagging).

Technical Skills:

  • Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Hugging Face Transformers).
  • Strong grasp of model fine-tuning, transfer learning, and prompt engineering techniques.
  • Experience with cloud platforms (AWS, GCP, Azure) for model deployment and scaling.
  • Knowledge of modern NLP libraries and frameworks, including spaCy, NLTK, and OpenNLP.
  • Familiarity with MLOps practices and tools such as MLflow, Airflow, and DVC for experiment tracking and model management.

Soft Skills:

  • Strong problem-solving and critical-thinking skills.
  • Ability to work in an agile, fast-paced environment with minimal supervision.
  • Excellent communication and teamwork abilities.

Preferred Qualifications

  • Experience with multimodal AI and integrating text with other data types (e.g., images, audio).
  • Knowledge of prompt tuning, reinforcement learning from human feedback (RLHF), and continuous learning techniques for LLMs.
  • Contributions to open-source AI/NLP projects or published research in the field of generative AI.

This job has expired.

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