AI Engineer I/II/III/Sr./Expert

EQT is creating a dedicated AI team to move ideas into production — keeping us on the leading edge of AI, engaging our teams, and delivering real value across the business. As an AI Engineer, you’ll be a foundational member of this team, helping define how AI is built, deployed, and adopted across one of the energy industry’s most forward-thinking companies.

We’re looking for an outcomes-oriented engineer—creative, disciplined, and execution minded. You’ll partner with data, product, and business teams to deliver scalable, secure models, pipelines, and infrastructure. You’ll collaborate across teams to create and implement models, pipelines, and infrastructure that enable intelligent automation and drive data-informed decisions at EQT.

The AI Engineer I/II/III/Sr./Expert responsibilities include but are not limited to:

• Design, build, and deploy AI/ML models end-to-end: from data exploration and feature engineering through training, evaluation, and productionization.
• Operate models in production with observability for drift, bias, data quality, and service health; ensure reproducibility, versioning, and governance across data, code, models, and prompts.
• Develop and maintain ML infrastructure (pipelines, jobs, orchestration, CI/CD) that scales with our needs.
• Evaluate and tune algorithms for accuracy, efficiency, cost, and fairness.
• Build generative AI and agent-based solutions (e.g., RAG pipelines, orchestration frameworks) that extend how our teams work and make decisions.
• Leverage industry-standard AI tools and managed services when they improve speed, quality, or cost, while ensuring alignment with EQT’s architecture and security practices.
• Partner across functions to shape requirements, SLAs, and success metrics; communicate outcomes in a way that drives understanding and adoption.
• Stay current with advancements in AI/ML and bring forward ideas aligned with EQT’s architecture, strategy, and evolving business priorities.

Required Experience and Skills:

• Bachelor’s degree in technical discipline (e.g., computer science, engineering, mathematics) or equivalent combination of education and experience.
• Experience designing, building, and operating ML systems in production; strong Python and software engineering fundamentals (git, code reviews, testing, packaging).
• Hands-on with Microsoft Azure and Databricks (notebooks/jobs, Delta Lake, Spark, Unity Catalog, MLflow/Model Registry, and model serving patterns).
• Solid data engineering skills (advanced SQL, Spark optimization, data modeling, pipeline orchestration, and performance/cost tuning).
• Applied machine learning expertise (feature engineering, supervised/unsupervised learning, rigorous evaluation, bias/fairness mitigation).
• Experience building LLM/RAG and agentic applications (embeddings, vector indexes, prompt/policy design, evaluation harnesses).
• MLOps competency (CI/CD for ML, containerization, secrets/config management, monitoring/alerting).
• Strong communicator—able to translate technical details into business impact, document outcomes clearly (e.g., READMEs, technical specs, lightweight dashboards/notebooks), and collaborate effectively across technical and non-technical teams.

Preferred Experience and Skills:

• Preferred candidates will have at least 1-2 years of hands-on experience building AI solutions.
• Working knowledge of Salesforce (Flow, Agentforce) and Databricks ecosystem extras (DBSQL, Mosaic AI, Serverless, Model Serving).
• Experience deploying AI agents at scale — managing orchestration, performance, and safety across multiple environments.
• Familiarity with Azure Foundry and large-scale enterprise AI deployment patterns.
• Experience with responsible AI practices (PII handling, safety guardrails, adversarial testing, and human-in-the-loop review).
• Streaming and event-driven ML (Structured Streaming, Delta Live Tables) and feature store practices.
• Performance optimization for inference (vector store tuning, caching, batching, quantization, accelerator-aware serving).
• Experiment tracking and monitoring tools (e.g., MLflow, Evidently, Arize).
• Infrastructure-as-Code and orchestration (e.g., Terraform, Bicep, Airflow, Databricks Workflows).

Remote work is being considered for this role excluding the following states:  California, Connecticut, Delaware, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, and Tennessee.