Machine learning Architect

DataNimbus Inc.; Hyderabad; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

DataNimbus Inc. in Hyderabad is hiring a senior data scientist to build and scale production-grade data science workloads, apply MLOps best practices, and contribute to Data+AI solutions across financial services and industrial domains.

You will work with cross-functional teams to translate complex data challenges into robust, scalable models that deliver measurable outcomes for customers. Responsibilities include developing LLM-based solutions on client data, exploring RAG architectures on

Qualifications

• 7–12 years of hands-on industry data science experience with ML/DL
• Experience with production-grade ML deployments on cloud platforms (AWS/Azure/GCP) including drift monitoring
• Experience with NLP techniques including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, LangChain, and OpenAI
• Ability to communicate and teach technical concepts to both technical and non-technical audiences
• 4+ years of customer-facing experience in a pre-sales or post-sales role
• Can meet expectations for technical training and role-specific outcomes within 3 months of hire.

Responsibilities

• Build and optimize customer data science workloads and apply the best MLOps to productionize these workloads across a variety of domains
• Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation
• Advise data teams on architecture, tooling, and best practices
• Provide technical mentorship to the broader ML SME community

Skills

Data science

MLops

NLP

RAG

Communication

Pre-sales experience

Tools

Apache Spark

Databricks

TensorFlow/PyTorch

HuggingFace

LangChain

OpenAI

MLflow

pandas

Job description

DataNimbus is an enterprise AI company with deep vertical solutions in financial services and industrial sectors. We build domain-native AI that understands how industries actually operate, not just how data flows through them. Our solutions are outcome-driven and industry-specific, delivering measurable results across how money moves, how assets perform, and how businesses grow. We go deep so our customers don’t have to start from scratch.

Why Join DataNimbus?

At DataNimbus, we believe in shaping a sustainable, AI-driven future while offering an environment that prioritizes learning, innovation, and growth. Our core values—Customer-Centricity, Simplicity, Curiosity, Responsibility, and Adaptability—are the foundation of our workplace, ensuring every team member can make a meaningful impact. Joining DataNimbus means being part of a dynamic team where you can:

• Work with cutting-edge technologies and revolutionize workflows in Data+AI solutions.
• Contribute to solutions that are trusted by global businesses for their scalability, security, and efficiency.
• Grow personally and professionally in a culture that values curiosity and continuous learning.

If you’re passionate about innovation, ready to solve complex challenges with simplicity, and eager to make a difference, DataNimbus is the place for you.

What do we want you to do:

• Build and increase customer data science workloads and apply the best MLOps to productionize these workloads across a variety of domains
• Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation
• Advise data teams on several data science such as architecture, tooling, and best practices
• Provide technical mentorship to the larger ML Subject Matter Expert community

What would help make your case:

• 7 – 12 years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, mlflow, scikit-learn, gensim, nltk, and TensorFlow/PyTorch
• Experience building production-grade machine learning deployments on AWS, Azure, or GCP including drift monitoring
• Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
• Experience communicating and teaching technical concepts to non-technical and technical audiences alike
• Passion for collaboration, life-long learning, and driving value through ML
• [Preferred] Experience working with Apache Spark™ to process large-scale distributed datasets
• [Preferred] Experience working with the Databricks platform
• [Preferred] 4+ years customer-facing experience in a pre-sales or post-sales role
• Can meet expectations for technical training and role-specific outcomes within 3 months of hire.