About TP
TP Group, formerly Teleperformance, is a global leader in digital business services with nearly 47 years of industry experience. As we evolve into the AI era, our Data, Technology & AI function builds the products and platforms that power smarter decisions across the enterprise.
About the Role
The Lead Data & AI Engineer is the senior technical authority for AI and data engineering. You will lead the design and delivery of production AI systems spanning agentic AI, LLM applications, machine learning, and the data foundations that feed them. You will set the engineering standards the team builds against, partner with business stakeholders to shape strategy, and personally own the architecture for the most complex initiatives.
This is a builder's leadership role. You write code, review architecture, sign off on critical designs, and stay close enough to the technology to make sharp calls in real time.
Key ResponsibilitiesTechnical Leadership and Architecture
- Serve as the dual AI and data SME for the function and the escalation point for complex technical decisions.
- Define and uphold engineering standards, design patterns, and best practices; conduct architecture reviews and provide sign-off on major deliverables.
- Lead technical discovery for new use cases. Evaluate feasibility, recommend approaches, and produce architecture documents that align stakeholders.
- Mentor junior and mid-level engineers through pair programming, code reviews, and design sessions.
Agentic AI and LLM Engineering
- Architect and ship production agentic AI systems including multi-agent orchestration and tool-use frameworks.
- Design advanced RAG architectures including hybrid search, re-ranking, query decomposition, and self-reflective retrieval.
- Build LLM evaluation frameworks and benchmark Claude, GPT, and Copilot against business KPIs.
- Own LLM integration end-to-end: API design, latency, token cost, rate limits, and fallback strategies.
Machine Learning and MLOps
- Lead the full ML lifecycle from problem framing through deployment, monitoring, and retraining.
- Deliver ML solutions across NLP, time-series forecasting, anomaly detection, and classification.
- Stand up MLOps pipelines for training, versioning, A/B testing, and drift detection.
- Embed explainability, fairness, and responsible AI controls into every production model.
Data Engineering and Solution Integration
- Architect ingestion and transformation patterns for AI workloads, including unstructured inputs, streaming, and late-arriving data.
- Integrate LLMs, ML models, vector stores, pipelines, and business APIs into cohesive production products.
- Define data contracts and standards; champion a data-as-a-product mindset.
- Partner with Power BI and reporting teams to translate model outputs into executive-ready insights.
What You Bring
- Bachelor's or Master's in Computer Science, Data Science, AI/ML, Engineering, or a related discipline.
- 8 to 12 years of hands-on experience in AI/ML engineering, applied data science, or LLM engineering, with at least 3 years in a technical leadership or architect capacity.
- Proven record of shipping production AI systems with measurable business impact, not just prototypes.
- Strong classical ML fundamentals alongside modern generative AI depth.
- Experience with enterprise data governance platforms (Unity Catalog, Collibra, or equivalent).
- Hands-on experience with responsible AI practices: fairness, explainability, content safety, prompt injection mitigation.
- Databricks Certified ML Professional, Azure AI Engineer Associate, or equivalent certification preferred.
Technical Skill Requirements
LLM Engineering
- Claude, GPT, Azure OpenAI; advanced prompt engineering, function calling, structured outputs
- LLM evaluation frameworks and eval harnesses
- Fine-tuning techniques (LoRA, PEFT) and working knowledge of RLHF/DPO
Agentic and RAG
- LangChain, LangGraph, AutoGen, Semantic Kernel, or CrewAI
- Multi-agent orchestration, tool routing, memory and reflection patterns
- Advanced RAG patterns, embedding model selection, vector databases (Mosaic AI Vector Search, Pinecone, or equivalent)
Machine Learning
- Scikit-learn, XGBoost, HuggingFace Transformers, PyTorch
- NLP, time-series, anomaly detection, classification
- Explainability (SHAP, LIME), fairness assessment, MLOps tooling
Cloud and Data Platform
- Azure as primary: Azure OpenAI, Azure ML, Databricks, AKS, Functions, APIM
- PySpark, Delta Lake, Unity Catalog, lakehouse and medallion design
- Streaming and batch ingestion (Event Hubs, Kafka, Auto Loader)
Programming and Engineering
- Python (expert), SQL (advanced)
- REST API design, FastAPI, async patterns
- Git, CI/CD, Docker, Kubernetes
Leadership and Communication
- Architecture review, design documentation, ADRs
- Technical mentoring, cross-functional stakeholder communication
- Vendor and platform evaluation
