AI/ML Engineer

Language(s): English

Work Schedule: 40 hours per work (US EST timezone)

Project Details

Client is looking for an AI/ML Engineer with 8-10 years of hands-on experience, including experience in solution architecture. Knowledge of Generative AI solutions and agentic frameworks is required.

Must have skills:

• Strong experience with NLP and chatbot development
• Hands-on work with LLMs (BERT, GPT-type models) and Generative AI
• Strong Python skills (mandatory)
• Experience training, fine-tuning, and evaluating ML/NLP models
• Data preprocessing and text analysis experience
• Hands-on experience with AWS (S3, EC2, SageMaker)
• Ability to build and manage API integrations
• Experience integrating with enterprise systems (SAP, Salesforce, ServiceNow - preferred)
• Strong communication and teamwork skills

5+ years of relevant AI/ML/NLP experience

Full job spec below:

Job Description

Machine Learning Engineer/Data Scientist

MAJOR JOB DUTIES AND RESPONSIBILITIES (List in order of importance)

NLP Application Development: Design, develop, and deploy an intelligent chatbot/virtual assistant using state-of-the-art language models and NLP techniques. Ensure the application responds accurately to user queries and maintains context throughout the interaction.

Working across client teams to develop and architect Generative AI solutions using ML and GenAI

Developing and promoting standards across the community

Evaluating and selecting appropriate AI tools and machine learning models for tasks, as well as building and training working versions of those models using Python and other open-source technologies

Help democratize the AI building process while advancing the capabilities of autonomous and human-controlled agents.

Setup and train large language models (BERT, GPT-3, etc.) and other state-of-the-art neural networks

Data Preprocessing: Clean, structure, and preprocess raw data to make it suitable for use with NLP models. This includes text normalization, tokenization, and other data transformation techniques.

Model Training and Validation: Train and fine-tune NLP models using appropriate datasets. Validate model performance using suitable metrics and techniques, adjusting model parameters as necessary to optimize performance.

Integration: Work on integrating the NLP capabilities with various platforms including SAP, Salesforce, SharePoint, and ServiceNow. Ensure smooth and efficient data exchange between systems.

Performance Optimization: Regularly monitor and evaluate the performance of the chatbot/virtual assistant. Use analytics to identify areas for improvement and optimize the system for better user experience and accuracy.

Collaboration: Collaborate with other engineers, data scientists, and business analysts to understand requirements, refine models, and implement NLP solutions.

Keeping Current with Industry Trends: Stay up-to-date with the latest advancements in NLP, machine learning, and AI. Evaluate and adopt new technologies, tools, and methodologies as appropriate to improve our capabilities.

QUALIFICATIONS (Education/Training, Experience and Certifications)

Bachelors or Masters degree in Mathematics, Statistics, Computer Science, Data Science, AI, Machine Learning, or a related field. 5+ years of professional experience in NLP/LLM solutions, with a focus on developing chatbots/virtual assistants or similar applications 5 Years of experience in programming Python/R Proficiency in industry-leading NLP tools, libraries, and frameworks

KNOWLEDGE SKILLS AND ABILITIES (Those necessary to perform the job competently)

Experience in deep learning and training LLMs. Expertise in programming languages commonly used in NLP, such as Python. Hands on experience with forecasting, predictive modelling, and classification. Hands on experience with AWS services, such as S3, EC2, SageMaker Exposure to Generative AI/OpenAI ChatGPT/LangChain Hands-on experience integrating with platforms like SAP, Salesforce, SharePoint, and ServiceNow is a strong advantage. Strong understanding of NLP concepts and techniques, such as tokenization, entity recognition, sentiment analysis, and language modelling. Strong understanding in building and maintaining API solutions. Excellent problem-solving skills, with the ability to troubleshoot technical challenges and develop effective solutions. Strong communication skills, with the ability to explain complex NLP concepts to non-technical stakeholders. A collaborative team player …