Senior Engineer AI

Litmus7; Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Litmus7 in Bengaluru, India seeks an experienced ML engineer to design, develop, and deploy cutting-edge AI systems. You will work on NLP tasks, generative AI, and real-world applications with a focus on responsible AI and scalable ML pipelines.

You will collaborate with data scientists and software engineers, implement MLOps practices, manage model training and deployment across cloud platforms, and push the boundaries of transformers, diffusion models, and text generation for large datasets.

Qualifications

• Minimum 5 years of experience in machine learning engineering and AI development.
• Deep expertise in ML algorithms and techniques including supervised/unsupervised learning, deep learning, reinforcement learning.
• Solid experience in natural language processing (NLP) - language models, text generation, sentiment analysis.
• Proven understanding of generative AI concepts like text/image/audio synthesis, diffusion models, transformers.
• Expertise in Agentic AI and building real world applications using the same.
• Experience working with Agentic AI frameworks like Langgraph, ADK, Autogen.
• Hands-on experience in developing and deploying generative AI applications (text generation, conversational AI, image synthesis).
• Experience in MLOps and ML model deployment pipelines.
• Proficiency in Python and ML frameworks like TensorFlow, PyTorch.
• Knowledge of cloud platforms (AWS, GCP, Azure) and tools for scalable ML solution deployment.
• Experience with data processing, feature engineering, and model training on large datasets.
• Familiarity with responsible AI practices, AI ethics, model governance and risk mitigation.
• Understanding of software engineering best practices and applying them to ML systems.
• Design, develop and optimize ML models for applications across domains.
• Build NLP pipelines for text generation, summarization, translation, etc.
• Develop and deploy cutting-edge generative AI & Agentic AI applications.
• Implement MLOps practices - model training, evaluation, deployment, monitoring, and maintenance.

Skills

Machine Learning Engineering

NLP

Generative AI

Agentic AI

MLOps

Python

TensorFlow

PyTorch

Cloud Platforms

Data Processing

Feature Engineering

Responsible AI

Transformers

Diffusion Models

Text Generation

Sentiment Analysis

Tools

Langgraph

ADK

Autogen

TensorFlow

PyTorch

Job description

Job Description:

• Minimum 5 years of experience in machine learning engineering and AI development.


• Deep expertise in machine learning algorithms and techniques like supervised/unsupervised learning, deep learning, reinforcement learning, etc.


• Solid experience in natural language processing (NLP) - language models, text generation, sentiment analysis, etc.


• Proven understanding of generative AI concepts like text/image/audio synthesis, diffusion models, transformers, etc.


• Expertise in Agentic AI and building real world applications using the same.


• Experience working with Agentic AI frameworks like Langgraph , ADK, Autogen etc.


• Hands-on experience in developing and deploying generative AI applications (text generation, conversational AI, image synthesis, etc.)


• Experience in MLOps and ML model deployment pipelines.


• Proficiency in programming languages like Python, and ML frameworks like TensorFlow, PyTorch, etc.


• Knowledge of cloud platforms (AWS, GCP, Azure) and tools for scalable ML solution deployment.


• Experience with data processing, feature engineering, and model training on large datasets.


• Familiarity with responsible AI practices, AI ethics, model governance and risk mitigation.


• Understanding of software engineering best practices and applying them to ML systems.


• Design, develop and optimize machine learning models for applications across different domains.


• Build natural language processing pipelines for tasks like text generation, summarization, translation, etc


• Develop and deploy cutting-edge generative AI & Agentic AI applications.


• Implement MLOps practices - model training, evaluation, deployment, monitoring, and maintenance.