Lead, AI Engineer

Were looking for a Senior AI Data Scientist to design, build, and operationalize agentic AI systems for our customers marketing analytics practice. This is a hands-on engineering role focused on building production-grade LLM agents, tools, and pipelines - not a research or architecture-strategy role. Youll work as a data scientist to turn LLM/agentic capabilities into deployed, monitored, and reliable systems.

Key Responsibilities:

• Design and build agentic AI systems - multi-step, tool-using agents that automate marketing analytics workflows (segmentation narratives, campaign insight generation, reporting, data QA).
• Develop and deploy Claude Skills, custom tools, and function-calling workflows to extend agent capabilities within defined guardrails.
• Build and maintain Retrieval-Augmented Generation (RAG) pipelines - chunking, embedding, vector store design, retrieval tuning, and grounding strategies for internal marketing/media data.
• Implement LLM-as-judge and other automated evaluation frameworks to score agent/model outputs for accuracy, hallucination, and consistency at scale.
• Own LLMOps/AIOps practices: prompt versioning, model/version rollout strategy, cost and latency monitoring, output drift detection, and automated regression testing for LLM pipelines.
• Apply MLOps discipline to AI systems - CI/CD for ML/LLM pipelines, containerization (Docker/Kubernetes), experiment tracking (MLflow, WB), and reproducible deployment across cloud platforms (AWS, GCP, Azure).
• Integrate LLM APIs (Anthropic Claude, OpenAI) into production pipelines, including structured output handling, function/tool calling, and multi-agent orchestration.
• Build evaluation harnesses and guardrails for agentic systems - output validation, safety checks, fallback logic, and human-in-the-loop review points.
• Write clean, production-quality Python and SQL to support data extraction, pipeline development, and agent tooling.
• Collaborate with data scientists to embed agentic/LLM components into existing marketing mix, segmentation, and targeting workflows.
• Document architecture, prompt design decisions, and evaluation methodology for reproducibility and team knowledge-sharing.
• Stay current with the fast-moving agentic AI ecosystem and proactively bring in relevant tools, frameworks, and techniques. Qualifications

• Atleast 4 years of overall AI/ML experience out if which at least 2 years of Generative AI solutions.
• Strong background in applied ML, data science, LLM and Agentic AI Engineering Systems with demonstrated delivery and client facing experience.
• Deep expertise in evaluation design, metrics, and dataset curation for LLM systems.
• Proven experience in model selection and prompt engineering, including structured output and tool-use prompting.
• Strong proficiency in Python and major ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Strong experience in LLM fine-tuning, RAG Context Engineering, Claude Code, Open AI Codex, Agentic Workflows.
• Strong RAG design choices (chunking, embeddings, retrieval strategies, reranking) and how to evaluate them.
• Must have implemented Agentic AI SDLC
• Working with GenAI on Azure, AWS, or Snowflake involves leveraging cloud-native AI tools-such as Azure OpenAI, AWS Bedrock, or Snowflake Cortex-to build or consume intelligent solutions directly on governed data.
• Experience on vibe coding - such as AntiGravity, Cursor, and VS Code is highly desirable.
• Proven ability to build end-to-end GenAI MVPs in Python (RAG/agents + evaluation harness) and prepare them for production handoff.
• Excellent communication and stakeholder management skills with a strategic mindset.