Role : Machine Learning Engineer - AI Research

Location : Bangalore

Employment Type : Full-time

Experience : 3+ Years

About the Role :

We are looking for a Machine Learning Engineer - AI Research with strong hands-on experience in deep learning, large language models, and model development. The role involves building, fine-tuning, evaluating, and improving AI models for complex, high-accuracy applications.

You will work closely with product, engineering, and domain experts to solve ambiguous AI problems and take solutions from research and experimentation to production.

Key Responsibilities :

- Model Training & Fine-Tuning : Train and fine-tune deep learning and LLM models using domain-specific datasets.

- Model Evaluation : Design benchmarks, evaluation frameworks, metrics, and error-analysis processes to measure model quality and reliability.

- Reinforcement Learning : Develop and implement RL/RLHF and preference-optimization approaches such as PPO, DPO, or similar techniques.

- Research & Experimentation : Conduct structured experiments across models, datasets, architectures, and training approaches while maintaining reproducible experiment records.

- Problem Solving : Independently identify, scope, and solve open-ended ML problems while balancing accuracy, latency, and infrastructure costs.

- Productionization : Translate successful research experiments into reliable, production-ready ML systems.

- Cross-Functional Collaboration : Work with product, engineering, and domain experts to convert real-world requirements into effective AI solutions.

What We're Looking For :

- 3+ years of hands-on experience building, training, and deploying deep learning models in research or production environments.

- Strong experience with deep learning and LLMs, including model training and fine-tuning.

- Practical experience with data curation, model training infrastructure, and large-scale experimentation.

- Experience designing and implementing ML evaluation and benchmarking frameworks.

- Working knowledge of RL, RLHF, PPO, DPO, or preference optimization techniques.

- Strong software engineering and problem-solving fundamentals.

- Experience with experiment tracking tools such as Weights & Biases (W&B), MLflow, or similar platforms.

- Ability to independently work on technically ambiguous problems and take ownership from idea to implementation.

- Strong analytical and communication skills.

Preferred :

- Experience with NLP, Legal AI, document intelligence, or LLM applications.

- Exposure to AI applications in regulated or high-stakes domains.

- Experience taking ML research prototypes into production environments.

What You'll Get :

- Opportunity to work on AI-first products involving LLMs and advanced machine learning.

- High ownership and the opportunity to work on challenging, open-ended ML problems.

- Direct collaboration with experienced product, engineering, and domain teams.

- Opportunity to contribute across the full lifecycle - from research and experimentation to production deployment.

- Competitive compensation based on experience and expertise.