Data and Applied Scientist Specialist/Expert

What You Build

• Lead architecture and delivery of enterprise-grade AI/ML and Generative AI solutions, including LLM-powered applications, intelligent assistants, and AI copilots.
• Define the enterprise AI/GenAI strategy aligned to business transformation, and evaluate emerging foundation models, frameworks, and technologies.
• Design and implement scalable LLM applications and agentic systems using commercial and open-source models; integrate vector databases, semantic search, prompt orchestration, tool/function calling, memory, and context-aware reasoning.
• Build robust RAG pipelines and knowledge retrieval systems; optimize prompts, inference, and evaluation frameworks; drive fine-tuning, alignment, and model optimization.
• Establish LLMOps practices for deployment, monitoring, observability, governance, and guardrails.
• Architect end-to-end AI platforms: data ingestion/processing, training/inference, orchestration frameworks, vector stores/embeddings, MLOps/LLMOps, Responsible AI, security, and compliance-across AWS, Azure, GCP, SAP BTP, and hybrid.
• Lead research and incubation: publish white papers, file patents, build PoCs/accelerators/reusable frameworks, and collaborate with academia, labs, startups, and cross-functional RD teams.
• Drive stakeholder alignment and technical governance; mentor data scientists, ML engineers, prompt engineers, and solution architects; partner with product, research, and business teams.

What You Bring

• 14+ years in Data Science, AI/ML, Generative AI, and Enterprise Architecture with a track record delivering production-grade LLM solutions.
• Degree in Computer Science, AI, Data Science, Engineering, or related field; PhD preferred for research-intensive profiles.

Technical expertise

• AI/ML: Deep learning, LLMs, Generative AI/foundation models, NLP/conversational AI, predictive analytics, and recommendation systems; reinforcement learning (preferred).
• LLM GenAI: Prompt engineering/optimization, RAG, AI agents/multi-agent systems, embeddings/semantic search, fine-tuning/adaptation, model evaluation/guardrails, LLMOps and observability.
• Frameworks Tools: LangChain, LlamaIndex, Semantic Kernel, Hugging Face, OpenAI APIs; vector databases (Pinecone, Weaviate, ChromaDB, FAISS); TensorFlow, PyTorch, scikit-learn.
• Architecture Platforms: Enterprise/solution architecture, distributed systems, microservices/APIs, cloud AI (AWS, Azure, GCP, SAP BTP), Kubernetes/Docker, CI/CD for AI.
• Programming Data: Python, SQL, R; Spark/Databricks; data engineering and big-data ecosystems.

Preferred

• Publications, patents, open-source contributions, innovation/incubator or RD experience.
• Hands-on in GenAI transformation, enterprise search, copilots, or autonomous agents.
• Strong understanding of Responsible AI, governance, and AI security.

Core strengths

• Strategic, innovation-driven mindset; excellent problem-solving.
• Clear communication and executive-level storytelling.
• Influence across senior leadership and customers; commitment to continuous learning.

Success measures

• Scalable AI/LLM solutions in production with measurable business impact.
• Innovation outcomes (adoption, reusability), research/patent contributions.
• Platform maturity: governance, reliability, cost/perf efficiency.
• Team capability growth and mentorship outcomes.

Where You Belong

• Be part of a dynamic, mission-driven team using AI to enable seamless interactions between individuals and information.
• Work in a collaborative, learning-oriented culture that values diverse perspectives and encourages experimentation.
• Thrive in a global, inclusive environment with opportunities to partner across regions and disciplines.

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