Lead Data/AI Engineer

Who we’re looking for

• Deep experience with full stack Python development (FastAPI, Flask; SQL/NoSQL databases).
• Demonstrated expertise in prompt engineering for LLMs (OpenAI, Anthropic, open-source LLMs).
• Strong understanding of context engineering, including session management, vector search, and knowledge retrieval strategies.
• Hands-on experience integrating AI agents and LLMs into production systems.
• Proficiency with agentic/conversational flow frameworks such as LangGraph.
• Familiarity with Retrieval-Augmented Generation (RAG) pipelines and multi-agent orchestration.
• Experience with cloud infrastructure, containerization (Docker), and CI/CD practices.
• Exceptional analytical, problem-solving, and communication skills.
• Familiarity with data engineering technologies (e.g., Spark, Kafka, Databricks, Snowflake) and additional languages (e.g., Java, Scala, SQL) as needed for pipeline integration.
• Experience with API development patterns (e.g., HTTP/REST, GraphQL) and microservice architecture.
• Strong experience with AI-powered IDEs and tools such as Visual Studio, GitHub Copilot, Windsurf, Cursor, etc.
• Good experience with fine-tuning language models is a PLUS.
• Strong foundations in statistics and machine learning (probability, hypothesis testing, experimental design, bias/variance, regularization) and ability to translate ambiguous problems into measurable ML objectives.
• Hands-on experience building and validating predictive models end-to-end (feature engineering, training, cross-validation, model selection, error analysis) using tools like scikit-learn and XGBoost/LightGBM/CatBoost.
• Experience designing rigorous evaluation strategies: offline metrics, online experimentation (A/B tests), guardrails, and monitoring for drift/quality regressions.
• LLM evaluation expertise: building automated and human-in-the-loop evals for generation quality (task success, relevance, factuality/faithfulness, toxicity/safety), and using frameworks/patterns like LLM-as-a-judge with calibration and consistency checks.
• RAG-focused data science: measuring retrieval quality (recall@k, MRR, nDCG), diagnosing chunking/embedding tradeoffs, query rewriting, reranking, and grounding/attribution quality.
• Prompt and context optimization using experimental design: prompt A/B testing, prompt regression testing, sensitivity analysis, and systematic prompt/version management.
• Fine-tuning and adaptation experience (as a plus): LoRA/QLoRA, DPO/IPO-style preference optimization, dataset creation/curation, labeling guidelines, and contamination/leakage checks.
• Familiarity with LLM systems performance and cost analytics: token/call optimization, caching strategies, latency/throughput benchmarking, and model routing across providers/sizes.
• Experience with interpretability and debugging for GenAI systems (error taxonomy, hallucination analysis, attribution, and root-cause analysis across retrieval/model/prompt layers).
• Bachelor level degree or equivalent in Computer Science, or related field of study.

What you’ll do

• Design, optimize, and evaluate prompts for LLMs to achieve precise and contextually appropriate outputs across diverse use cases.
• Architect and implement dynamic context management strategies, including session memory, retrieval-augmented generation, and user personalization to enhance agent performance.
• Integrate, fine-tune, and orchestrate LLMs within Python-based applications, leveraging APIs and custom pipelines for scalable deployment.
• Build and manage complex conversational and agent workflows using frameworks like LangGraph to support multi-agent or multi-step solutions.
• Develop and maintain robust backend services, APIs, and (optionally) front-end interfaces to deliver end-to-end AI applications.
• Work closely with product, data science, and engineering teams to define requirements, run prompt experiments, and iterate quickly on solutions.
• Implement testing, monitoring, and evaluation pipelines to continuously improve prompt effectiveness, context handling, and overall agent performance.
• Apply and advocate best practices in software engineering, code quality, testing, DevOps, and secure, ethical AI automation.
• Translate AI use case requirements into effective data models and pipelines, ensuring data integrity through statistical quality procedures and advanced AI techniques.
• Evaluate and select optimal technologies for cloud and on-premise deployments, implementing strategies for scalability, performance monitoring, and cost optimization.

About the position

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.

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).