Principal AI Engineer - Vice President

The Digital Software Engineering Lead Analyst is a strategic technical leader responsible for designing and engineering enterprise grade *Agentic AI solutions* capable of integrating data from multiple heterogeneous systems and operating reliably at scale. You will act as a hands-on architect, engineer, and partner to cross functional teams—including Data Engineering, Architecture, Enterprise Platforms, and Product—defining the technical approach, AI system design, and integration patterns needed to build robustfault tolerantnt AI agents and AIdriven automation capabilities. This role requires deep technical breadth across machine learning, LLMs, data pipelines, cloud engineering, orchestration, and modern AI frameworks. The solutions you design will enable strategic automation, cognitive decisioning, and dynamic multi-agent workflows across the organization. **Key Responsibilities** AI Solution Architecture & Agentic Systems - Design and build agentic AI systems, including autonomous agents, multiagent orchestration, tool use, and adaptive decision-making workflows. - Architect fault tolerant, scalable AI solutions using modern agent frameworks (e.g., Google\_ADK, LangGraph, LangChain , OpenAI Assistants, CrewAI, AutoGen, custom orchestrators). - Define the end-to-end AI system blueprint, including knowledge integration, orchestration, pipelines, observability, governance, and failover strategies. - Evaluate and select LLMs, embeddings, vector stores, and middleware best suited for complex enterprise requirements. Data Integration & Pipeline Engineering - Partner with engineering teams to aggregate, ingest, and harmonize data from multiple systems, including APIs, databases, internal platforms, and unstructured sources. - Design robust data pipelines optimized for LLM workloads (e.g., chunking, metadata design, semantic indexing, retrieval strategies). - Implement mechanisms for ensuring data freshness, quality, and fault tolerance across distributed systems. LLM, RAG, and Generative AI Engineering - Build advanced Retrieval-Augmented Generation (RAG) architectures, including hybrid retrieval, query planning, and retrieval optimization. - Develop, tune, and deploy applications leveraging major LLMs (OpenAI, Gemini, Claude, Llama, Mistral, HuggingFace ecosystem). - Engineer prompts, system instructions, and reusable prompt templates for deterministic AI behavior. - Implement safety guardrails, evaluation pipelines, and bias/error mitigation strategies. AI Platform Engineering & Deployment - Develop cloudnative GenAI applications using containerized infrastructure (Kubernetes, OpenShift, Docker). - Build and support production-grade MLOps / AIOps pipelines, including CI/CD, automated testing, monitoring, model versioning, and rollback strategies. - Partner with engineering teams to ensure secure, compliant deployment of all AI workloads. Technical Leadership & Collaboration - Serve as technical SME for AI engineering patterns, solution design, and architecture. - Mentor mid-level engineers and analysts, guiding best practices in AI build patterns and engineering quality. - Influence product and platform strategy by providing insights on emerging GenAI and agentic technologies. **Qualification:** Experience - 10+ years of experience in software engineering, AI/ML engineering, systems architecture, or related fields. - Proven experience designing and deploying enterprisegrade AI Systems in production. Required Technical Skills Core AI/ML & GenAI Expertise - Strong foundations in ML, NLP, embeddings, statistics, neural networks, and LLMs. - Extensive handson experience with LLMs: Gemini, OpenAI, Claude, Mistral, Llama, opensource models, etc. - Deep expertise in RAG architectures, including retrieval optimization, vector search, and semantic data modeling. - Experience building agentic AI using Google\_ADK or langGraph Programming & Data Engineering - Strong proficiency in Python and libraries such as: Pandas, NumPy, scikitlearn,…