Apps Development Group Manager - Senior Vice President
About The Position
The Digital Software Engineering Lead Analyst is a strategic technical leader responsible for designing and engineering enterprise-grade agentic AI solutions that integrate data from multiple heterogeneous systems and operate reliably at scale. The role serves as a hands-on architect, engineer, and partner to cross-functional teams, defining technical approaches, AI system designs, and integration patterns for robust, fault-tolerant AI agents and AI-driven automation. The position requires broad technical expertise across machine learning, LLMs, data pipelines, cloud engineering, orchestration, and modern AI frameworks. Its solutions support strategic automation, cognitive decision-making, and dynamic multi-agent workflows across the organization. The role designs agentic AI architectures and data pipelines; develops RAG and generative AI applications; builds, deploys, and supports cloud-native AI platforms and production MLOps/AIOps pipelines; and promotes secure, compliant, observable, and responsible AI workloads. It also serves as a technical subject-matter expert, mentors engineers and analysts, and contributes to product and platform strategy.
Requirements
- 10+ years of experience in software engineering, AI/ML engineering, systems architecture, or related fields.
- Proven experience designing and deploying enterprise-grade AI systems in production.
- Strong foundations in machine learning, NLP, embeddings, statistics, neural networks, and LLMs.
- Extensive hands-on experience with LLMs, including Gemini, OpenAI, Claude, Mistral, Llama, or open-source models.
- Deep expertise in RAG architectures, including retrieval optimization, vector search, and semantic data modeling.
- Experience building agentic AI using Google ADK or LangGraph.
- Strong proficiency in Python and relevant libraries, including Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers, FastAPI, LangChain, and LlamaIndex.
- Hands-on experience with vector databases such as Pinecone, PGVector, MongoDB Atlas Vector Search, Neo4j, or Milvus.
- Experience building pipelines for large-scale unstructured data processing.
- Strong CI/CD experience with tools such as GitLab CI, Jenkins, Azure DevOps, ArgoCD, or GitHub Actions.
- Expertise deploying production generative AI solutions using Kubernetes, Docker, Helm, serverless runtimes, or cloud-native LLM services.
- Experience with monitoring, observability, and logging frameworks relevant to AI workloads.
- Exceptional problem-solving and analytical skills.
- Ability to execute independently and operate effectively in ambiguity.
- Strong collaboration skills across engineering, architecture, and product teams.
- Commitment to ethics, transparency, and responsible AI usage.
- Bachelor’s degree or equivalent experience required.
Nice To Haves
- Master’s degree preferred.
- Experience building AI systems in regulated or enterprise environments.
- Experience using knowledge graphs, graph databases, or enterprise metadata systems.
- Familiarity with AIOps, agent monitoring, or AI governance frameworks.
Responsibilities
- Design and build agentic AI systems, including autonomous agents, multi-agent orchestration, tool use, and adaptive decision-making workflows.
- Architect scalable, fault-tolerant AI solutions using modern agent frameworks.
- Define end-to-end AI system blueprints covering knowledge integration, orchestration, pipelines, observability, governance, and failover strategies.
- Evaluate and select LLMs, embeddings, vector stores, and middleware for enterprise requirements.
- Partner with engineering teams to aggregate, ingest, and harmonize data from APIs, databases, internal platforms, and unstructured sources.
- Design robust data pipelines optimized for LLM workloads, including chunking, metadata design, semantic indexing, and retrieval strategies.
- Implement mechanisms for data freshness, quality, and fault tolerance across distributed systems.
- Build advanced RAG architectures, including hybrid retrieval, query planning, and retrieval optimization.
- Develop, tune, and deploy applications using major LLMs and open-source models.
- Engineer prompts, system instructions, and reusable prompt templates for deterministic AI behavior.
- Implement safety guardrails, evaluation pipelines, and bias and error mitigation strategies.
- Develop cloud-native generative AI applications using containerized infrastructure.
- 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 and compliant deployment of AI workloads.
- Serve as a technical subject-matter expert for AI engineering patterns, solution design, and architecture.
- Mentor mid-level engineers and analysts on AI build patterns and engineering quality.
- Influence product and platform strategy by providing insights on emerging generative AI and agentic technologies.
Benefits
- Medical, dental, and vision coverage
- 401(k)
- Life, accident, and disability insurance
- Wellness programs
- Paid time off, including vacation, sick leave, and paid holidays
- Discretionary and formulaic incentive and retention awards for eligible employees
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What This Job Offers
Job Type
Full-time
Career Level
Senior
