Job Description

Join our dynamic AI engineering team to contribute to cutting-edge Generative AI projects (e.g., chatbots, recommendation engines, coaching assistants). You’ll support senior engineers in building, training, and deploying NLP models while learning advanced ML techniques. This role is ideal for a motivated individual eager to grow in AI/ML with hands-on experience in scalable systems.

In this role, you will…

LLM Development and Optimizations

• Assist in designing, fine-tuning, and optimizing LLMs for agentic workflows, content generation, and recommendation systems.
• Conduct data preprocessing, feature engineering, and exploratory analysis to enhance model performance.
• Implement and experiment with advanced deep learning techniques, including reinforcement learning and prompt engineering, for generative AI applications.

Deployment & Monitoring

• Support deployment of ML models into production environments.
• Help build monitoring tools to track model performance and data drift.

Collaboration

• Work with cross-functional teams (engineers, product managers) to deliver high-quality AI solutions.
• Document processes and present findings to stakeholders.

Continuous Learning

• Stay updated on advancements in generative AI, NLP, and ML frameworks (e.g., PyTorch, Hugging Face).
• Participate in code reviews and adopt best practices in ML ops.

You’ve Got What It Takes If You Have…

• Programming: Python (pandas, NumPy), SQL
• ML/NLP: Familiarity with PyTorch, transformers, embeddings, and attention mechanisms and basics of GenAI
• Tools: Git, Docker, cloud platforms (AWS /GCP)
• Data: Handling Data cleaning, preprocessing, and visualization (Matplotlib/Seaborn)
• Deployment: Basic knowledge of REST APIs, CI/CD pipelines

Soft Skills

• Collaborative Mindset: Team player who thrives in a fast-paced, results-driven environment.
• Adaptability: Willingness to learn new tools/technologies (e.g., LangChain, vector databases).
• Problem-Solving: Analytical thinker with attention to detail.
• Communication: Ability to explain technical concepts to non-technical stakeholders.

Qualifications

• Education: Bachelor’s degree in Computer Science, Data Science, or related field.
• Experience: 0–2 years in ML/NLP projects (academic/internship experience accepted).

Extra dose of awesome if you have...

• Certifications in ML/AI (e.g., TensorFlow Developer, AWS ML Specialty).
• Exposure to generative AI tools (e.g., GPT, Claude, Llama).