Analytical AI Engineer - II

Overview

Build and maintain analytical and machine learning models, dashboards, and semantic layers/knowledge graphs that turn business questions into data-driven insights, and apply Generative AI and LLM-based techniques to make analytics outputs consumable by AI Agent solutions and downstream applications, under the guidance of senior team members.

What you'll do

• Build and maintain analytical and machine learning models for forecasting, classification, regression, anomaly detection, and other data-driven decision-making use cases, under the guidance of senior team members.
• Collaborate with business stakeholders to understand requirements, translate business questions into analytical tasks, and apply statistical methods to generate insights.
• Build and maintain analytical dashboards, and contribute to semantic layer development to support consistent, reusable metrics and self-service reporting.
• Contribute to the design and development of semantic layers, ontologies, and knowledge graphs, and help make them accessible and consumable for AI Agent solutions and downstream applications.
• Build semantic and context layers (business definitions, metrics, entity relationships, metadata, and curated retrieval context), and consume them through Agentic AI and Generative AI solutions so AI agents and LLM applications can deliver accurate, grounded, and explainable insights.
• Build and maintain analytics and ML pipelines, from data extraction, cleaning, and feature engineering through model development, validation, and deployment.
• Develop and support data pipelines for analytical modeling and reporting needs.
• Apply Generative AI and LLM-based techniques where relevant to support analytics workflows.
• Track experiments and manage model versions using experiment-tracking tools.
• Write clean, well-documented, production-ready code for analytical modeling and pipeline development.
• Use AI coding assistants to accelerate development productivity.
• Manage source code and version control, and contribute to CI/CD workflows.
• Collaborate with team members following Agile/Scrum methodologies.

What you'll need

• Bachelor's Degree in Computer Science, Statistics, Mathematics, Data Science, or a related field.
• 3+ years of progressively responsible experience in analytics, data science, or applied AI/ML roles.
• Solid grounding in statistics and probability, with working knowledge of experimental design.
• Experience with Databricks for data pipeline development and analytical/ML workloads.
• Working knowledge of PySpark for distributed data processing.
• Familiarity with MLflow (or equivalent) for experiment tracking.
• Experience building analytical dashboards; exposure to semantic layer concepts.
• Experience or hands-on exposure to building semantic layers, ontologies, or knowledge graphs, and structuring them for consumption by AI Agent or GenAI-based applications.
• Experience with core ML libraries and frameworks including Scikit-learn, NumPy, Pandas, PyTorch, TensorFlow/Keras, etc.
• Proficiency in SQL and experience working with structured and semi-structured datasets.
• Strong Python programming skills for analytical modeling and pipeline development.
• Proficiency with Git for version control.

Nice to have

• Hands-on experience building forecasting, classification, regression, and anomaly detection models with libraries such as Scikit-learn, PyTorch, and TensorFlow/Keras, iterating under senior guidance from problem framing through model evaluation.
• Comfort partnering directly with business stakeholders to turn open-ended questions into testable hypotheses, then running A/B tests and other hypothesis-testing designs in Python / SQL to quantify impact and communicate findings back in business terms.
• Experience building and maintaining production dashboards (e.g. in Tableau, Power BI, or Looker) backed by a shared semantic layer.
• Exposure to modeling semantic layers, ontologies, and knowledge graphs (e.g. with RDF, OWL, SPARQL, or Neo4j) structured so an AI Agent or downstream GenAI application can query and reason over them.
• Working experience standing up end-to-end pipelines on Databricks as a single reproducible workflow.
• Practical use of PySpark to process large, distributed datasets efficiently, including partitioning and join strategies.
• Applied exposure to Generative AI and LLM-based techniques (prompting, summarization, retrieval-style workflows), ideally with familiarity with Azure OpenAI, AWS Bedrock, or Google Vertex AI.
• Disciplined use of MLflow (or equivalent) to log experiments, compare runs, and version models.
• Regular use of VS Code (or similar) paired with AI coding tools such as GitHub Copilot or Claude Code.
• Solid Git / GitHub workflow habits (branching, PRs, code review) and comfort contributing to CI/CD pipelines.
• Experience working within Agile/Scrum ceremonies as part of a cross-functional analytics team.
• Exposure to GenAI and Agentic frameworks such as LangChain, LangGraph, Pydantic AI, LlamaIndex, etc.
• Familiarity with LLM platforms such as Azure OpenAI, AWS Bedrock, Google Vertex AI, etc.
• Experience with knowledge graph or ontology tools (e.g., RDF, OWL, SPARQL, Neo4j, or similar).
• Experience with BI/visualization tools (e.g., Tableau, Power BI, Looker).
• Exposure to cloud platforms (AWS, Azure, or GCP) for analytics or model deployment.

Details

• Location: Hyderabad, Telangana, India.
• Work model: the assigned work model is based on the role's core responsibilities and is not stated in the posting.