Machine Learning Architect

4 weeks 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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