Formee Holdings - Machine Learning Engineer - Conversational AI

Job Title : Machine Learning Engineer.

Reporting To : Technical Lead.

Job Type : Full-time, Permanent.

Location : Whitefield, Bangalore.

ABOUT THE COMPANY :

Formee Holdings is a multinational organization developing a portfolio of new and exciting brands in the tech and retail spaces.

Formee Express, Formee, Tela Mela and Formee Jewlery are all brands under our umbrella; our fields of business are in e-commerce, EdTech and F&B industries.

We are currently headquartered in Melbourne, Australia, with offices also in India, Malaysia, Canada, China, Philippines, US, and UK.

Needless to say, our teams are diverse and innovative!

At Formee Holdings, we value independence, flexibility, career and personal growth.

We're a fast-growing Edu-tech company that embraces innovation and encourages our team members to push boundaries and reach new heights.

With Formee Holdings, you will get an opportunity to work with a diverse team in different parts of the world.

There is a huge potential for learning and growth.

JOB OVERVIEW :

We are looking for a Machine Learning Engineer to design and build a conversational AI chatbot for public use.

The chatbot will provide accurate, empathetic responses to a diverse audience, handling a wide range of queries while ensuring scalability, security, and accessibility.

Youll collaborate with our technology and UX teams to create a tool that aligns with Formees mission of community support.

The ideal candidate will have a strong background in building a conversational AI model and a passion for applying AI techniques to solve real-world problems.

KEY RESPONSIBILITIES :

Model Development & Innovation :

- Design, prototype, and optimize scalable deep learning architectures focused on natural language understanding and generation.

- Leverage techniques such as reinforcement learning from human feedback (RLHF) and instruction tuning to enhance conversational performance.

- Experiment with prompt engineering and develop methodologies to improve model interpretability and alignment with user intent.

Research & Experimentation :

- Stay abreast of advances in NLP, deep learning, and generative AI by reviewing academic literature and industry trends.

- Lead proof-of-concept experiments and iterative development cycles to assess and validate novel approaches.

System Integration & Deployment :

- Collaborate with software and data engineers to integrate AI models into production pipelines using MLOps best practices.

- Monitor live systems for model drift and other performance issues; implement mechanisms for continuous improvement and retraining.

Cross-Functional Leadership :

- Work closely with product, design, and business teams to translate customer feedback and market trends into actionable technical requirements.

- Help set strategic roadmaps for AI research and guide development efforts in line with ethical AI principles and regulatory compliance.

SKILLS REQUIRED :

Technical Expertise :

- Expertise in designing and training a deep learning model from scratch using frameworks such as Tensorflow or PyTorch.

- Hands-on experience in designing, training, and fine-tuning large-scale Neural Language Models.

- In-depth knowledge of deep learning architectures, particularly Transformer models, and familiarity with techniques like reinforcement learning from human feedback (RLHF) and instruction tuning.

- Proficiency in natural language processing (NLP) fundamentals, including attention mechanisms, context window management, and prompt engineering.

- Strong programming skills in Python and familiarity with modern data pipelines.

Experimental & Research Acumen :

- Demonstrable track record in conducting rigorous, iterative experiments such as hyperparameter tuning, ablation studies, and performance benchmarking to optimize model performance and alignment.

- A history of research contributions, which could include peer-reviewed publications, open-source contributions, or patents in areas related to AI, deep learning, or NLP.

Additional Skills and Traits :

- Strong mathematical foundation, especially in linear algebra, calculus, probability, and statistics, to support algorithm development and optimization.

- Ability to dive into complex, large codebases to debug, optimize, and iterate on models.