Senior Data/AI Engineer

At AT&T we’re redefining the future of communication by connecting people to greater possibility - with expertise, simplicity, and inspiration. At the heart of our purpose lies a diverse workforce of 140,000 people and a culture that aspires to serve customers first, act boldly, move faster, and win as one.

Our Product Development group, part of AT&T’s Technology Services (ATS) organization, is responsible for building software-based products, services, and platforms that our customers love and need. Harnessing technology and rebuilding software expertise, the team is inspiring simplicity with projects that deliver revenue and cost savings opportunities.

AT&T is building a new era of intelligent automation. We are seeking a Senior Data/AI Engineer with strong hands-on technical expertise in building, integrating, and deploying enterprise-grade AI systems. You will bring advanced skills in fullstack Python and experience with a modern data and AI engineering technology stack (e.g., Spark, Kafka, Databricks, Snowflake, Java, Scala, SQL) to design, develop, and deliver intelligent systems focused on large language models (LLMs), prompt engineering, and context management.

In this role, you'll contribute to designing context-rich AI solutions, crafting effective prompts, integrating agentic frameworks like LangGraph, and helping build robust data pipelines for scalable, high-performance, and cost-effective deployment.

As a Senior Data/AI Engineer, you will develop, deploy, and support context-driven AI solutions that transform how organizations work and deliver value. You’ll use the power of LLMs, Retrieval-Augmented Generation (RAG), and modern AI agents to automate complex workflows and drive productivity and efficiency. Your work will enable operations with autonomous AI agents, seamless data flows, and adaptive digital services.

Core Responsibilities

• Design, implement, and optimize prompts for LLMs to achieve accurate and contextually relevant outputs across various use cases.

• Develop and maintain context management strategies, including session memory and retrieval-augmented generation, to improve agent performance.

• Integrate and support fine-tuning of LLMs in Python-based applications, leveraging APIs and pipelines for scalable deployment.

• Build, test, and maintain conversational and agent workflows using frameworks like LangGraph for multi-agent or multi-step solutions.

• Develop and maintain backend services, APIs, and possibly front-end interfaces to deliver end-to-end AI applications.

• Work collaboratively with product, data science, and engineering teams to gather requirements, run prompt experiments, and iterate on solutions.

• Support testing, monitoring, and evaluation pipelines to continually improve prompt effectiveness, context handling, and system performance.

• Follow and promote best practices in software engineering, code quality, DevOps, testing, and secure, ethical AI practices.

• Translate AI use case requirements into effective data models and pipelines, helping to ensure data integrity via statistical quality checks.

• Support technology evaluations and contribute to platform choices for scalability, performance, and cost optimization.

Skills, Knowledge, and Experience

Day-to-Day:

• Design and develop agentic AI solutions and automation workflows using LLMs, prompt engineering, and context management.

• Collaborate with cross-functional teams to define requirements, conduct prompt experiments, and iterate on solutions.

• Build and support production-grade Python applications for AI agent deployment.

• Participate in code reviews, contribute to technical best practices, and share knowledge with team members.

• Monitor and help tune prompt and context strategies to enhance agent effectiveness.

Technical Skills:

• Extensive experience with fullstack Python development (FastAPI, Flask; SQL/NoSQL databases).

• Demonstrated expertise in prompt engineering for LLMs (OpenAI, Anthropic, open-source LLMs).

• Solid understanding of context engineering, including session management, vector search, and knowledge retrieval.

• Hands-on experience integrating AI agents and LLMs into production environments.

• Proficiency with agentic/conversational frameworks such as LangGraph.

• Familiarity with Retrieval-Augmented Generation (RAG) concepts and multi-agent orchestration.

• Experience with cloud infrastructure, containerization (Docker), and CI/CD.

• Strong analytical, problem-solving, and communication skills.

• Familiarity with data engineering technologies (e.g., Spark, Kafka, Databricks, Snowflake) and related languages (e.g., Java, Scala, SQL) for pipeline integration.

• Experience with API development patterns (e.g., HTTP/REST, GraphQL) and microservice architecture.

• Good experience with AI-powered IDEs and tools such as Visual Studio, GitHub Copilot, Windsurf, Cursor.

• Experience with fine-tuning language models is a PLUS.

Additional Experience in (Hard-Core Data Science + LLM/GenAI Data Science Experience) as a Plus:

• Solid grounding in statistics and machine learning (probability, hypothesis testing, experimental design, bias/variance, regularization) and the ability to translate business problems into measurable ML objectives.

• Experience building and validating predictive models end-to-end (feature engineering, training, cross-validation, model selection, error analysis) with tools like scikit-learn and XGBoost/LightGBM/CatBoost.

• Experience supporting evaluation strategies: offline metrics, experimentation (A/B testing), guardrails, and monitoring for drift/quality.

• Exposure to LLM evaluation frameworks (automated and human-in-the-loop), measuring generation quality (success, relevance, factuality, safety), and using evaluation patterns such as LLM-as-a-judge.

• Understanding of RAG-focused data science: retrieval evaluation (recall@k, MRR, nDCG), chunking/embedding tradeoffs, query rewriting, reranking, grounding.

• Experience with prompt and context optimization methods, including prompt A/B testing and prompt management.

• Experience (or interest) with fine-tuning and adaptation techniques (LoRA/QLoRA, DPO/IPO), dataset creation, and data labeling.

• Familiarity with LLM system performance and cost monitoring (token optimization, caching, benchmarking, model routing).

• Exposure to debugging and interpretability in GenAI systems (error analysis, hallucination investigations, attribution).

Education: 

Bachelor level degree or equivalent in Computer Science, or related field of study. 

Compensation:

Salary range for Slovakia is between 3 841EUR - 5 766EUR gross per month.

Salary range for Czech Republic is between 113 308CZK - 169 958CZK gross per month.

The individual starting salary within this range will depend on relevant factors such as the candidate’s experience, expertise, skills, and education or training.

Benefits: 

A career with us, a global leader in communications and technology, comes with big rewards. We offer a competitive salary plus an annual company performance bonus. Once you’re a part of the team, you’ll gain some amazing perks and benefits including wellness & leisure time contribution, sickness compensation plan, premium medical services, family friendly benefits as well as meal contribution and extra days of vacation (…and much more). 

Location: 

This is a hybrid-flex worker position where our preference is for you to be present with your team in the office at least 2 days per week.   

Ready to find out more? Apply today!   

Weekly Hours:

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It is the policy of AT&T to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, AT&T will provide reasonable accommodations for qualified individuals with disabilities. AT&T is a fair chance employer and does not initiate a background check until an offer is made.