Senior Data Scientist

Description

The company is a global leader in thermal management, mission-critical building systems, energy efficiency and decarbonization. Its customers in data centers, healthcare, pharmaceuticals, advanced manufacturing and higher education use its technology to consume energy more productively and cut carbon emissions.

The company has been operating for over 140 years and provides advanced technology and lifecycle services.

This senior role is on the team building an agentic AI platform: autonomous, tool-using agents that plan, reason and carry out multi-step business workflows from start to finish. The ideal person has solid ML foundations and hands-on experience building LLM applications, agent orchestration frameworks and Azure cloud services.

Responsibilities

• Build, ship and maintain production multi-agent systems covering planning, tool and function calling, memory and orchestration, using frameworks such as LangGraph, AutoGen, CrewAI or Semantic Kernel.
• Create end-to-end RAG pipelines: document ingestion, chunking, embeddings, vector search on Azure AI Search, FAISS or pgvector, re-ranking, and grounding agents in knowledge.
• Connect agents to enterprise systems and APIs through tool calling and Model Context Protocol (MCP) or similar connector patterns.
• Build and run evaluation frameworks for agent workflows: task completion, hallucination detection, trajectory analysis, LLM-as-judge pipelines and A/B tests.
• Put guardrails in place: prompt-injection defense, content filtering, role-based tool permissions, human-in-the-loop checkpoints and audit logging.
• Fine-tune and optimize LLMs (LoRA/PEFT, prompt optimization, model routing, latency and cost trade-offs) on Azure OpenAI and Azure AI Foundry.
• Design, build and assess classical ML models for classification, regression, forecasting and NLP where they support agentic workflows.
• Own LLMOps and MLOps for the platform: experiment tracking, prompt versioning, CI/CD, observability and tracing (LangSmith, Azure Monitor, OpenTelemetry), drift monitoring and retraining.
• Work with data engineers on data quality, availability and governance across Azure Data Lake, Databricks and Synapse Analytics.
• Turn unclear business problems into agentic AI solutions and present architecture decisions and results to senior stakeholders.
• Mentor junior data scientists, lead code and design reviews, and promote engineering best practices.

Requirements

• Professional experience in data science or ML, including building LLM or GenAI applications in production.
• Hands-on work with the LangChain or LangGraph agent frameworks.
• A solid grasp of LLM application patterns, including prompt engineering.
• Expert-level Python: pandas, NumPy, scikit-learn, async programming and FastAPI API development.
• Experience with vector databases and embeddings, such as Azure AI Search, Pinecone, Weaviate, Qdrant, FAISS or pgvector.
• Strong SQL skills and experience working with large-scale data.

Conditions

Hybrid working model that offers flexibility.

Benefits

• Pay that is competitive, plus performance-based incentives.
• Chance to design a greenfield agentic AI platform from scratch.
• Sponsorship for Azure and AI certifications, plus a continuous learning budget.
• Path into technical leadership, with mentoring opportunities.
• Access to leading GenAI tools, compute and cross-domain AI projects.
• A collaborative culture.