Looking For Senior ML and LLM Systems Engineer

5 - 10 years

8 - 18 Lacs

Posted:1 day ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Job Description

Key Responsibilities

ML Foundations & Infrastructure

  • Provide technical leadership on ML fundamentals, including algorithm design, optimization, generalization, and evaluation.
  • Build and evolve core ML infrastructure and scalable training pipelines spanning pretraining, fine-tuning, and inference.

LLM Architecture & Scaling

  • Architect and lead development of large language model systems, including embeddings, context management, memory, and inference.
  • Drive decisions on model design, scaling strategies, compute trade-offs, and training methodologies.
  • Evaluate and champion state-of-the-art architectures and production-ready model stacks.

LLM Fine-Tuning & Personalization

  • Lead LLM fine-tuning across tasks and domains using PEFT methods (LoRA/QLoRA), prompt tuning, adapters, and transfer learning.
  • Define metrics, evaluation workflows, and optimization processes to ensure robust, high-quality outputs.

Agentic & Autonomous AI Systems

  • Design and implement agentic and multi-agent workflows, including planning, tool integration, state management, and context retention.
  • Productionize autonomous systems with strong reliability, safety, and observability in collaboration with cross-functional teams.

Technical Leadership

  • Mentor engineers and applied researchers, ensuring architectural consistency and high implementation quality.
  • Lead design reviews, cross-team collaboration, and strategic technical planning.

Minimum Qualifications

  • PhD with 02 years or Master’s with 2+ years of relevant industry or applied research experience in ML/AI, CS, Statistics, or related fields.
  • Strong ML fundamentals and hands-on experience with deep learning frameworks (PyTorch/TensorFlow).
  • Proven experience with LLM training, fine-tuning, evaluation, or deployment.
  • Deep understanding of transformers, scaling principles, and performance trade-offs.
  • Experience designing agentic or autonomous AI systems.
  • Strong software engineering skills (MLOps, APIs, CI/CD, scalable platforms).
  • Excellent communication and technical leadership skills.

Preferred Qualifications

  • Advanced degree (MS/PhD) in a relevant field.
  • Experience deploying large-scale AI systems on cloud platforms (AWS, GCP, Azure).
  • Background in LLMOps, distributed training, or inference optimization.

What Success Looks Like

  • Scalable ML foundations underpinning all AI workflows.
  • Production-ready LLM infrastructure with continuous improvement pipelines.
  • Reliable, high-impact agentic systems driving business outcomes.
  • A strong, high-performing ML/AI team grown through your mentorship and leadership

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