VP, Applications Development - Tech Lead; Java, UI, PL-SQL & Agentic AI

Position: VP, Applications Development - Tech Lead (Java, UI, PL-SQL & Agentic AI)
We are seeking an experienced Senior Generative AI Developer to help drive the design, development, and integration of state-of-the-art Generative AI and  
** agentic AI
** solutions across our enterprise Controls Technology platform. You will collaborate with cross-functional teams, contribute deep technical expertise in context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration, and play a key role in delivering scalable, grounded AI solutions to enhance automation and operational efficiency. This role centers on architecting robust applications and agent systems on top of pre-trained and hosted foundation models - not on training or fine-tuning models.

** Key Responsibilities*
* + Collaborate with AI architects, leads, and stakeholders to design and implement generative and agentic AI solutions that address business challenges.

+ Architect advanced  
** context engineering
** strategies - context layering, chaining, compression, pruning/offloading, and memory management - to maximize reliability, provenance, and token efficiency in production.

+ Design and implement advanced generative AI methods, including sophisticated  
** prompt engineering
** and  
** Retrieval-Augmented Generation (RAG)** .

+ Build and optimize  
** RAG systems** , including hybrid search, multi-vector retrieval, and re-ranking pipelines.

+ Design and implement  
** knowledge graphs
** and  
** Graph RAG
** architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses for high-value business domains.

+ Architect  
** agentic workflows and multi-agent systems
** using  
** Google Agent Development Kit (ADK)
** and comparable frameworks (Lang Graph, Microsoft Agent Framework, CrewAI), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer.

+ Design robust  
** agent harnesses**  - governance, constraints, feedback loops, state/session management, and execution controls that make long-running agent systems reliable and safe.

+ Integrate agents with tools and data via the  
** Model Context Protocol (MCP)
** and orchestrate inter-agent collaboration and task delegation via the  
** Agent2

Agent (A2A)
** protocol.

+ Support the integration of GenAI and agentic applications into production environments, ensuring robust deployment, scalability, observability, and maintainability.

+ Contribute to the development and optimization of real-time and streaming AI solutions.

+ Stay current with the latest advances in generative and agentic AI and actively share knowledge with the team.

+ Ensure adherence to ethical AI guidelines, guardrails, agent isolation/sandboxing, data privacy, and compliance standards.

+ Mentor junior team members, provide code reviews, and foster a culture of technical excellence.

** Required Technical Skills*
* + Deep, hands-on expertise in  
** core generative AI concepts**  - foundation models, LLMs, embeddings, tokenization, and context-window management.

+ Advanced skills in  
** prompt engineering
** and  
** context engineering** , including familiarity with prompt design tools/frameworks and dynamic context orchestration.

+ Strong experience building  
** RAG systems** , including chunking strategies, hybrid search, and multi-vector retrieval.

+ Practical experience designing  
** knowledge graphs
** and  
** Graph RAG
** pipelines (e.g., using graph databases such as Neo4j or ArangoDB) for relationship-aware, multi-hop retrieval.

+ Proven experience building  
** agentic AI systems
** with  
** Google ADK
** and/or comparable frameworks (Lang Graph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK), including tool/function calling, planning, and memory.

+ Strong grasp of  
** multi-agent orchestration
** patterns (supervisor/worker, hierarchical, peer-to-peer) and  
** harness engineering**  (governance, feedback loops, execution controls, agent isolation/sandboxing).

+ Hands-on experience with  
** agent interoperability protocols**  - the  
** Model Context Protocol (MCP)
** for tool/data access and the  
** Agent2

Agent (A2A)
** protocol for inter-agent collaboration.

+

Experience with  
** agent observability and evaluation**  (e.g., tracing, Open Telemetry-based tooling) for production agent systems.

+ Proficiency with  
** major GenAI APIs**  (OpenAI, Gemini, Claude, etc.) and orchestration frameworks such as  
** Lang Chain and Llama Index** .

+ Strong skills in  
** NLP**  (NER, dependency parsing, text classification, topic modeling).

+ Proficiency with  
** vector databases
** and embedding models for large-scale retrieval.

+

Experience with containerization (
** Docker** ), orchestration (
** Kubernetes** ), and CI/CD pipelines for AI/agentic applications.

+ Solid understanding of AI compliance, guardrails, and responsible AI practices.

+ Strong skills in  
** Python
* * and experience with data preprocessing, document ingestion, and API development.

** Required Soft Skills*
* + Strong collaboration skills to work effectively in cross-functional teams.

+ Analytical and proactive…