SOURCEFUSE - MACHINE LEARNING ENGINEER

Job Summary Personal Characteristics

Strong portfolio and excellent attitude.

Must be self-confident to work in a Team and to

handle the responsibilities individually as well

Should be a good listener/ Can articulate well /

Good Communication Skills

Ability to work with teams across organizational

boundaries, different cultures and different time

zones in a virtual environment

Delivery oriented and able to work under strict

deadlines.

We are seeking an experienced Machine Learning to join our AI-driven

project. The ideal candidate will have a strong background in prompt

engineering techniques such as Tree of Thought (ToT) and Chain of

Thought (CoT), along with hands-on expertise in fine-tuning foundational

models using AWS services like Amazon SageMaker and AWS Bedrock.

The role requires a deep understanding of AI/ML workflows and the ability

to implement advanced prompt optimization methods to enhance model

performance.

Key Responsibilities

Design and implement advanced prompt engineering strategies, including ToT, CoT, and other optimization methods.

Fine-tune pre-trained foundational models using AWS services such as Amazon SageMaker and AWS Bedrock.

Develop and optimize ML workflows for efficient training, inference, and deployment.

Leverage JumpCloud for identity and security management within the ML environment.

Collaborate with data scientists, engineers, and business stakeholders to integrate AI-driven solutions. Monitor

model performance and continuously refine prompts and training methodologies for better accuracy. Stay

updated with the latest research and trends in prompt engineering and ML fine-tuning.

Required Qualifications

3+ years of experience in machine learning, AI, or NLP.

Proficiency in prompt engineering with a focus on Tree of Thought (ToT) and Chain of Thought (CoT).

Hands-on experience in fine-tuning and deploying models using Amazon SageMaker and AWS Bedrock.

Strong programming skills in Python, TensorFlow, PyTorch, or similar ML frameworks.

Experience working with AWS cloud services for model training and deployment.

Familiarity with JumpCloud and cloud-based identity/security management.

Strong analytical and problem-solving skills with an ability to work in cross-functional teams.

Preferred Qualifications

Experience in large-scale AI model training and optimization.

Knowledge of LLM architectures and optimization techniques.

Experience in data engineering and feature engineering for ML models.

Familiarity with MLOps best practices.