Lead AI Engineer- LLMOps/MLOps

We are looking for a Head of Engineering to lead our **AI** capability, spanning **AI platforms, machine learning systems, and emerging agentic AI architectures**. This role is responsible for driving **end-to-end solutioningfrom client discussions and problem framing to execution strategy and production delivery**. You will operate at the intersection of **client advisory, technical architecture, and execution leadership**, building scalable foundations that power advanced AI and autonomous systems. **Key Responsibilities** 1. Client Advisory & Problem Framing - Lead senior-level client conversations to identify opportunities across **data, AI, and agentic workflows** - Translate ambiguous business problems into structured **AI/data solution blueprints** - Act as a trusted advisor on **AI transformation roadmaps**, including platform and operating model design - Present architectures, trade-offs, and execution strategies to technical and non-technical stakeholders 2. Data & AI Foundations - Define and drive architecture for **modern data platforms** (data lakes, lakehouses, real-time pipelines) - Build scalable **ML and AI foundations** including feature stores, training pipelines, and inference systems - Establish reusable frameworks, accelerators, and reference architectures - Ensure strong governance across **data quality, lineage, security, and compliance** 3. Agentic AI Systems - Lead design and implementation of **agentic AI systems** (LLM-based agents, RAG pipelines, multi-agent orchestration) - Define patterns for **tool use, memory, reasoning workflows, and human-in-the-loop systems** - Evaluate and integrate emerging GenAI technologies into production-grade solutions - Drive experimentation industrialization of agentic use cases 4. Execution Strategy & Delivery Leadership - Own end-to-end delivery: **discovery PoC MVP scaled production** - Build and lead cross-functional teams across **data engineering, ML engineering, and AI engineering** - Define delivery plans, milestones, and measurable outcomes - Manage risks, dependencies, and client expectations in complex engagements 5. Engineering Excellence & MLOps - Establish best practices across **CI/CD, MLOps, LLMOps, and observability** - Ensure robustness in **model performance, monitoring, retraining, and lifecycle management** - Drive standards for **reproducibility, evaluation, and system reliability** 6. Capability Building & Influence - Build internal capability across **AI foundations and agentic systems** - Mentor engineering leaders and shape technical culture - Contribute to **thought leadership, GTM assets, and reusable IP** - Influence senior stakeholders across client and internal ecosystems **Qualifications** - 15 to 20 years in engineering across **data, platforms, and AI/ML systems** - 5+ years in leadership roles managing large, distributed engineering teams - Strong experience in **data platforms (batch + streaming), cloud ecosystems (AWS/Azure/GCP)** - Deep expertise in **ML systems, MLOps, and production AI architectures** - Hands-on experience with **Generative AI and LLM-based systems (RAG, agents, orchestration frameworks)** - Proven ability to lead **client-facing engagements and drive delivery from concept to production** - Excellent communication and executive stakeholder management skills **Preferred Qualifications** - Experience in consulting / AI services organizations - Exposure to building **agentic workflows in enterprise settings** - Familiarity with tools like LangChain, LlamaIndex, vector databases, orchestration frameworks - Experience across multiple industries and large-scale transformations Note: This is a third party job (Aggregated by careeruplift.ai). Shortlisting and Final hiring decision & process is handled by the company.