ML Architect

DataNimbus Inc.; Hyderabad; On-site; INR 1,800,000 - 3,400,000; Full time

Job summary

DataNimbus Inc. in Hyderabad, India is seeking a senior data scientist to build and scale customer data science workloads, applying robust MLOps to productionize models across multiple domains.

You will develop LLM solutions on customer data—RAG architectures, natural language querying of structured data, and content generation—while guiding data teams on architecture and tooling and mentoring the ML community.

Qualifications

• 6–10 years of hands-on industry data science experience with ML tooling and production workflows.
• Experience building production-grade ML deployments on AWS, Azure, or GCP including drift monitoring.
• Experience with NLP techniques including vector databases, fine-tuning LLMs, and deploying LLMs with HuggingFace/Langchain/OpenAI.
• Ability to communicate and teach technical concepts to both non-technical and technical audiences.

Responsibilities

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

Skills

pandas

mlflow

scikit-learn

gensim

nltk

TensorFlow/PyTorch

LLM deployment

drift monitoring

HuggingFace

Langchain

OpenAI

Databricks

Apache Spark

Vector databases

Tools

HuggingFace

Langchain

OpenAI

Databricks

Apache Spark

Job description

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:

• 6 – 10 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.