Senior Machine Learning Engineer (6+ Years Experience)

Overview

We are looking for a Senior Machine Learning Engineer to lead the development and optimization of Small Language Models (SLMs) for enterprise clients. You will drive complex model fine-tuning, knowledge distillation. As a senior technical contributor, you will mentor junior engineers, guide model selection and GPU optimization decisions, and ensure high-quality delivery across multiple concurrent client engagements.

Key Responsibilities

• Lead and execute complex fine-tuning and knowledge distillation pipelines for Small Language Models (1B-13B parameters) including Llama, Mistral, Phi, Qwen, and Gemma model families.
• Architect and implement production-grade RAG systems with vector database integration for domain-specific enterprise applications.
• Drive model selection decisions by evaluating performance, licensing, and deployment requirements across client use cases in FinTech, Healthcare, Insurance, and Retail verticals.
• Collaborate with MLOps engineers to optimize inference performance, including quantization (INT8/INT4), latency tuning, and GPU resource utilization on AWS infrastructure (EC2, SageMaker, EKS).
• Design and generate high-quality synthetic datasets for model training, addressing data privacy constraints and domain-specific requirements.
• Provide technical mentorship to mid-level ML engineers — guiding experimentation, reviewing code, and establishing ML best practices across the pod.
• Evaluate emerging SLM architectures, fine-tuning techniques, and optimization frameworks to maintain client's competitive edge in the market.
• Support pre-sales activities by contributing to technical assessments, model benchmarking, and solution design for client proposals.
• Contribute to the development of pre-built domain-specific SLMs for priority verticals, enabling rapid deployment for future customers.
• Stay updated on SLM research, new model releases, and fine-tuning best practices through paper reading and team knowledge sessions.
  
  

• Engineering degree in Computer Science, Mathematics, Electrical Engineering, or related field.
• 6+ years of experience in applied ML, deep learning, or AI systems engineering.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain).
• Proven experience with model compression, distillation, and retrieval-augmented generation workflows.
• Solid understanding of data engineering, vector databases, and modern LLM architectures.
• Excellent problem-solving, collaboration, and communication skills.
• Prior experience mentoring or leading junior engineers is a strong plus.