# Senior ML Engineer

**Company:** [6sense](https://hotfix.jobs/companies/6sense)
**Location:** Bengaluru, India
**Role:** ML Engineering
**Experience:** 6+ years
**Skills:** Machine Learning, Natural Language Processing, LLMs, Transformers, Embeddings, Retrieval-Augmented Generation, LangGraph, LangChain, Amazon Bedrock, Python, AWS, Databricks, MLOps, PyTorch, TensorFlow
**Posted:** 2026-08-24

> Build and deploy explainable machine learning, NLP, LLM, and agentic systems that power enterprise go-to-market intelligence products. The role requires 6+ years of production ML experience, strong Python and cloud skills, and end-to-end ownership from modeling through monitoring.

## Job Description

## Responsibilities
- Own machine learning problems end to end, from data exploration and modeling through deployment, monitoring, and production iteration.
- Build NLP, LLM, and agentic systems at enterprise scale, including retrieval-based architectures and multi-agent workflows.
- Develop explainable ranking, recommendation, prediction, and optimization models.
- Partner with Product and Go-to-Market to turn ambiguous business problems into shipped capabilities.
- Improve the performance, scalability, and reliability of production ML systems and help shape AI platform architecture.
- Explain technical work to technical and non-technical audiences and engage with customers when needed.
- Mentor engineers and raise the bar for engineering excellence.

## Requirements
- 6+ years of industry experience building and deploying machine learning systems in production, with clear end-to-end ownership.
- Strong foundation in machine learning and applied statistics, with hands-on depth in NLP, transformers, embeddings, and retrieval-based systems.
- Practical experience with modern generative AI tooling such as LangGraph, LangChain, or Amazon Bedrock.
- Strong Python skills and experience building distributed ML pipelines on cloud infrastructure such as AWS or Databricks.
- Solid understanding of feature engineering, model evaluation, and MLOps practices.
- Product mindset focused on building customer-facing AI products and measuring customer impact.
- Excellent communication and ability to explain complex technical work clearly.
- Comfort with ambiguity and ability to drive execution independently.

## Nice to Have
- Experience with RAG architectures, vector databases, and prompt engineering.
- Hands-on experience with PyTorch or TensorFlow.
- Background in B2B SaaS, enterprise AI products, or forward-deployed engineering, especially involving complex customer data and rapid delivery.

## Compensation and Benefits
- Health coverage.
- Paid parental leave.
- Generous paid time off and holidays.
- Quarterly self-care days off.
- Stock options.
- Equipment and support for working from home or in an office.
- Learning and development initiatives, including access to LinkedIn Learning.
- Wellness education sessions, wellness days, and employee resource group events.

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