ML Engineer III

Slintel; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

6sense is seeking an ML Engineer III to join the Data Science team. This role targets strong ML, NLP, applied AI, and production-ready solutions.

You will collaborate with PMs, engineers, and stakeholders to deploy end-to-end ML systems, including GenAI and RAG pipelines, across customer-facing applications.

The ideal candidate is execution-focused, curious, and capable of solving business problems with modern AI platforms.

Qualifications

• 4-6 years of production ML experience.
• Strong foundation in ML, statistics, experimentation.
• Experience building predictive models, recommender systems, classifiers, or ranking models.
• Hands-on NLP, embeddings, transformer models, retrieval systems.
• Python with ML libraries: scikit-learn, PyTorch, TensorFlow, XGBoost.
• Distributed processing with Spark or Databricks.
• Familiar with model deployment and ML lifecycle.
• Independent execution of projects amid ambiguity.
• Strong problem-solving and communication.
• Ability to collaborate across Data Science, Product, Engineering.

Responsibilities

• Design, develop, and deploy ML models and AI solutions for business and customer-facing applications.
• Build NLP and transformer-based models for classification, ranking, recommendation, prediction, and retrieval use cases.
• Contribute to GenAI and Agentic AI initiatives, including RAG pipelines, prompt engineering, tool usage, and workflow orchestration.
• Perform data exploration, feature engineering, model training, evaluation, and performance analysis.
• Develop scalable data pipelines and production workflows for model training and inference.
• Work with structured and unstructured data sources to generate actionable insights and build predictive systems.
• Partner with Product, Engineering, and Analytics teams to translate business requirements into technical solutions.
• Monitor model performance in production and contribute to model retraining, evaluation, and continuous improvement processes.
• Participate in design reviews, experimentation, and technical discussions to improve system quality and reliability.
• Document solutions, communicate findings, and present recommendations to both technical and non-technical stakeholders.
• Contribute to best practices in machine learning development, testing, deployment, and observability.

Skills

Machine learning fundamentals

NLP techniques

Python data science

Distributed data processing

Model deployment & MLOps

Problem solving & communication

Cross-functional collaboration

Tools

Scikit-learn

PyTorch

TensorFlow

XGBoost

Apache Spark

Databricks

Job description

Job Summary

We are looking for a ML Engineer III to join the Data Science team at 6sense. This role is ideal for someone with a strong foundation in machine learning, NLP, and applied AI who can independently solve business problems and deliver production-ready solutions.

As a ML Engineer III, you will work closely with senior ML engineers, product managers, engineers, and business stakeholders to build intelligent systems that improve customer outcomes and drive business impact. You will contribute across the full machine learning lifecycle, from problem formulation and experimentation to deployment and monitoring.

The ideal candidate is technically strong, curious, execution-focused, and eager to work on modern AI systems including LLM-powered applications, retrieval systems, and intelligent automation workflows.

What You'll Do

• Design, develop, and deploy machine learning models and AI solutions for business and customer-facing applications.
• Build and optimize NLP and transformer-based models for classification, ranking, recommendation, prediction, and retrieval use cases.
• Contribute to GenAI and Agentic AI initiatives, including RAG pipelines, prompt engineering, tool usage, and workflow orchestration.
• Perform data exploration, feature engineering, model training, evaluation, and performance analysis.
• Develop scalable data pipelines and production workflows for model training and inference.
• Work with structured and unstructured data sources to generate actionable insights and build predictive systems.
• Partner with Product, Engineering, and Analytics teams to translate business requirements into technical solutions.
• Monitor model performance in production and contribute to model retraining, evaluation, and continuous improvement processes.
• Participate in design reviews, experimentation, and technical discussions to improve system quality and reliability.
• Document solutions, communicate findings, and present recommendations to both technical and non-technical stakeholders.
• Contribute to best practices in machine learning development, testing, deployment, and observability.

What We're Looking For

Required Qualifications

• 4-6 years of experience building and deploying machine learning systems in production environments.
• Strong foundation in machine learning, statistics, experimentation, and applied data science.
• Experience developing predictive models, recommendation systems, classification models, ranking models, or related ML applications.
• Hands-on experience with NLP techniques, embeddings, transformer models, and retrieval systems.
• Experience working with Python and common machine learning libraries such as Scikit-learn, PyTorch, TensorFlow, XGBoost, or similar.
• Experience working with distributed data processing frameworks such as Spark or Databricks.
• Familiarity with model deployment, monitoring, and ML lifecycle management.
• Ability to independently execute projects with moderate ambiguity and deliver high-quality solutions.
• Strong problem-solving, analytical thinking, and communication skills.
• Ability to collaborate effectively across Data Science, Product, and Engineering teams.

Preferred Qualifications

• Experience working with LLMs, prompt engineering, RAG systems, or agentic AI workflows.
• Familiarity with LangGraph, LangChain, Amazon Bedrock, OpenAI, Anthropic, or similar AI platforms.
• Experience with vector databases, embeddings, and semantic search systems.
• Experience with cloud platforms such as AWS.
• Experience in B2B SaaS, MarTech, AdTech, or customer-facing AI products.
• Contributions to technical blogs, open-source projects, research publications, or internal technical communities.