Job Summary

As a Senior ML Engineer, you will work across a broad set of machine learning areas. This includes building retrieval-driven AI agents that ground LLM outputs in trusted SP data, as well as developing and operating production-grade machine learning systems that power our essential AI toolkits.

What We Are Looking For

- Bachelor's degree or higher in Computer Science, Engineering, or a related field.
- 3+ years of significant, hands-on industry experience with machine learning, natural language processing (NLP), and information retrieval systems, including designing, shipping, and maintaining production systems.
- Strong proficiency in Python.
- Experience reading and understanding SQL databases and writing queries for specific access patterns.
- Proven experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation.
- Demonstrated effective coding, documentation, collaboration, and communication habits.
- Strong problem-solving skills and a proactive approach to addressing challenges.
- Ability to adapt to a fast-paced and dynamic work environment.
- Experience working with machine learning libraries/frameworks for Large Language Model (LLM) orchestration, such as LangChain.
- (Preferred) Experience working with RAG based system

Ideal for

- Enjoy building robust, production-grade ML systems end to end
- Have strong ML fundamentals, with a practical understanding of the backend and deployment work needed to run these systems in production
- Want to work across data, retrieval, modeling, and system design

What You'll Do

- Develop Advanced ML Systems: Create, refine, and deploy machine learning systems that solve complex business problems and power Kensho products.
- Build Retrieval-Driven AI Agents: Design AI agents that fetch, validate,



and structure data from SP datasets, ensuring answers produced by LLMs are grounded in SP's data universe.
- Evaluate LLM-based Agents: Identify and resolve performance gaps in both online and offline settings, addressing issues such as performance, latency, memory usage, compute efficiency, and feature consistency.
- Work With Domain Specific Data: Leverage proprietary structured and unstructured datasets, deep dive to have domain understanding, work with Subject Matter Experts (SMEs).
- Scale ML Applications: Optimize and scale ML systems to support high demand, effective resource utilization, and reliable production behavior.
- Reduce Technical Debt: Proactively identify areas of the stack that can be improved, and propose solutions that strengthen reliability and maintainability.
- Taking Initiative: Scope, plan, and execute ML initiatives that develop core capabilities across Kensho products.
- Collaborate Across Teams: Work closely with Data, Product, Design, and Engineering teams to ensure smooth operations and contribute to long-term product vision.
- Improve User Experiences: Partner with Product and Design to develop ML-driven functionality that enhances user workflows and aligns with business needs.
- Drive the ML Lifecycle: Engage in all phases of the ML lifecycle, from problem framing and data exploration to model deployment and production monitoring, ensuring continuous improvement.

Technologies We Love

- Traditional ML: Scikit-learn, XGBoost, LightGBM
- ML/Deep Learning: PyTorch, Transformers, HuggingFace, LangChain
- Deployment tools such as: Docker, Amazon EKS, Jenkins, AWS
- EDA/Visualization: Pandas, Matplotlib, Jupyter, Weights Biases
- Tools/Toolkits: DVC, MosaicML, NVIDIA NeMo, LabelBox
- Techniques: RAG, Prompt Engineering, Information Retrieval, Data Embedding
- Datastores: Postgres, OpenSearch, SQLite, S3

📌 Machine Learning Engineer (Hyderabad)
🏢 S&P Global Market Intelligence
📍 Hyderabad