Experience:-7 - 9 years Engagement Type:- Contract To Hire Duration:- 9 Months Engagement Mode:-Hybrid Location(s) :- Mumbai, Maharashtra, India Position: ML Architect Position Overview:
We are seeking an experienced ML Architect to build and scale customer data science workloads, applying best-in-class MLOps practices to productionize solutions across a variety of domains. The role involves developing cutting-edge LLM solutions including RAG architectures on enterprise knowledge repositories, natural language querying of structured data, and content generation.
The ML Architect will serve as a trusted advisor to data teams on architecture, tooling, and best practices, while also providing technical mentorship to the broader ML Subject Matter Expert community. The ideal candidate brings deep hands-on data science expertise, solid communication skills, and a passion for driving business value through machine learning.
Key Responsibilities:
- Build and scale customer data science workloads and apply best MLOps practices to productionize these workloads across a variety of domains.
- Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repositories.
- Implement natural language querying of structured data and content generation solutions.
- Advise data teams on data science architecture, tooling, and best practices.
- Provide technical mentorship to the larger ML Subject Matter Expert community.
- Communicate and teach technical concepts to both non-technical and technical audiences.
- Meet expectations for technical training and role-specific outcomes within 3 months of hire.
Required Skills:
- 6 10 years of hands-on industry data science experience
- Pandas
- MLflow
- Scikit-learn
- Gensim
- NLTK
- TensorFlow
- PyTorch
- Experience building production-grade machine learning deployments on AWS, Azure, or GCP
- Drift monitoring for ML deployments
- Vector databases
- Fine-tuning LLMs
- Deploying LLMs
- HuggingFace
- LangChain
- OpenAI
- Graduate degree in Computer Science, Engineering, Statistics, Operations Research, or equivalent practical experience
- Experience communicating and teaching technical concepts to non-technical and technical audiences
- Passion for collaboration, lifelong learning, and driving value through ML.