Machine Learning Engineer

Exterro India; Chennai District; On-site; INR 800,000 - 1,200,000; Full time

Job summary

Exterro India is seeking a Machine Learning Engineer with 1-3 years of experience to design, build, and deploy ML models focusing on GenAI, LLMs, and NLP.

You will work with software engineers to productionize models, optimize algorithms, and translate business problems into scalableAI solutions.

This role requires strong Python skills and hands-on experience with PyTorch or TensorFlow, NLP toolkits, and modern ML deployment practices.

Qualifications

• 1-3 years of professional experience in machine learning engineering.
• Design, build and deploy ML models with GenAI/LLMs/NLP focus.
• Experience with Python, ML frameworks, and model deployment.

Responsibilities

• Design, develop, and implement scalable ML models and algorithms.
• Collaborate with software engineers to productionize ML models.
• Fine-tune models for improved performance and accuracy.
• Explore GenAI techniques and AI agents for real-world problems.
• Document architectures, experiments, and results.

Skills

ML engineering

GenAI

LLMs

NLP

Python

PyTorch

TensorFlow

NLP frameworks

Git

Data structures

Algorithms

Job description

Job Profile & Description

Machine Learning is a field of artificial intelligence that focuses on developing systems that can learn and improve from experience.

Machine Learning Engineers work on creating and implementing AI models and algorithms to solve complex problems. This role often involves working with large datasets, developing algorithms, and deploying models into production environments.

We are seeking an enthusiastic and motivated Machine Learning Engineer with 1-3 years of experience to join our team. As a Machine Learning Engineer, you will be responsible for designing, building, and maintaining machine learning models with a focus on Generative AI, Large Language Models (LLMs), and Natural Language Processing (NLP). You will use your skills and knowledge to tackle real-world challenges and contribute to the advancement of AI technology.

Duties and Responsibilities

• Design, develop, and implement efficient and scalable machine learning models and algorithms.
• Work closely with software engineers to integrate ML models into production systems.
• Implement and optimize ML algorithms and pipelines.
• Optimize and fine-tune existing models for improved performance.
• Contribute to the research and implementation of state-of-the-art GenAI techniques.
• Create AI agents designed to tackle real-life problems.
• Harness internal and external knowledge systems to enhance model intelligence.
• Participate in the full machine learning lifecycle, from data preparation to model deployment.
• Collaborate with cross-functional teams to identify and solve complex business problems using AI.
• Stay up-to-date with the latest advancements in ML, especially in GenAI, LLMs, and NLP.
• Document your work, including model architectures, experiments, and results.

Must-Have Skills

• 1-3 years of professional experience in machine learning engineering.
• Proficiency in designing and implementing machine learning models, particularly in GenAI, LLM, and NLP domains.
• Hands-on experience in developing and deploying agentic AI solutions to solve complex, real-world problems.
• Strong proficiency in Python and either PyTorch or TensorFlow for building and training models.
• Strong programming skills in Python, including knowledge of data structures, algorithms, and object-oriented programming principles.
• Experience with NLP techniques and frameworks (e.g., NLTK, spaCy, Hugging Face Transformers).
• Familiarity with GenAI and LLM technologies (e.g., GPT, Llama, Mistral).
• Strong problem-solving skills and ability to translate business requirements into technical solutions.
• Proficiency in version control systems (e.g., Git).

Good-to-Have Skills

• Experience in writing SQL queries and working with relational databases.
• Experience in integrating or productionising machine learning models to the existing frameworks.
• Experience in back-end development to integrate machine learning models with applications (Full stack experience is an extra bonus to have).
• Experience with data engineering tools and techniques for large-scale data processing.
• Experience with cloud platforms (e.g., AWS, GCP, or Azure) for ML model deployment.
• Understanding of serving technologies (e.g., FastAPI, Flask API, ONNX Runtime).
• Familiarity with ML experiment tracking tools (e.g., MLflow, Weights & Biases).
• Strong desire to stay updated with the latest advancements in machine learning and AI.
• Familiarity with Kubernetes, Docker, and Jenkins for model deployment and pipeline automation.