Looking For Senior ML and LLM Systems Engineer

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