Only 24h Left: Senior Machine Learning Engineer

1 week ago

Bengaluru, India Tata Consultancy Services Full time

Senior Machine Learning Engineer

Experience Range: 10 - 16 years

Location: Bengaluru

Key Responsibilities

- Collaborate with product managers, stakeholders, and engineering teams to define hypotheses and gather requirements.
- Design, develop, and deploy robust, scalable machine learning models for production applications.
- Research and integrate state-of-the-art GenAI models (e.g. GPT, T5, BERT, diffusion models, GANs) into business use cases.
- Drive innovation in areas such as conversational AI, media synthesis, personalisation, and agentic systems.
- Fine-tune and customise foundation models for specific domains and tasks.
- Implement best practices for GenAI safety, alignment, and ethical deployment.
- Stay current with GenAI research, open-source models, and industry trends.
- Lead ML components from ideation to deployment, collaborating across distributed teams.
- Design frameworks for agent development and cross-agent communication.
- Collaborate with software engineers, data scientists, and product managers to define success metrics.
- Review code, provide feedback, and mentor junior team members.
- Communicate technical concepts and project updates to stakeholders across varying technical backgrounds.

Basic Qualifications

- Minimum 10 years of experience in machine learning engineering or a related role.
- Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers).
- Experience with supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), model evaluation, and data preparation.
- Strong understanding of MLOps, model monitoring, retraining, and guard railing.
- Excellent written and verbal communication skills.
- Ability to work independently and manage project-based goals across time zones.

Preferred Qualifications

- Experience with agentic architectures and multi-agent systems.
- Familiarity with GenAI safety frameworks and ethical deployment strategies.
- Exposure to enterprise-scale ML environments and agile delivery models.

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