Senior Machine Learning Engineer (6+ Years Experience)

Awign; Bengaluru Urban; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Awign in Bengaluru North, India, seeks a Senior Machine Learning Engineer to lead development and optimization of Small Language Models for enterprise clients. You will mentor juniors, drive fine-tuning, knowledge distillation, RAG systems, and GPU optimization on AWS, delivering high-quality, multi-client solutions.

You will evaluate model performance and deployment factors, coordinate with MLOps, and contribute to pre-sales with technical benchmarks and domain-specific SLMs for rapid customer

Qualifications

• 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.

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.

Skills

Python

PyTorch

TensorFlow

Hugging Face

LangChain

MLOps

RAG systems

Vector databases

GPU optimization

Data engineering

Model compression

Distillation

Mentoring

Communication

Education

Engineering degree in Computer Science, Mathematics, Electrical Engineering, or related field

Tools

AWS SageMaker

EC2

Kubernetes

Job description

Bangalore North, India | Posted on 02/13/2026

Role 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.

Qualifications

• 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.