Partner directly with enterprise clients to understand business objectives, identify AI opportunities, and translate requirements into scalable GenAI and agentic AI solutions.

Lead technical discovery sessions, architecture workshops, and solution design discussions with client stakeholders and cross-functional teams.

Design end-to-end AI architectures covering LLM integration, retrieval-augmented generation (RAG), agent orchestration, data processing, APIs, and enterprise system integration.

Develop solution blueprints, technical specifications, architecture diagrams, and implementation roadmaps aligned with client requirements and business outcomes.

Present technical recommendations, architectural trade-offs, and implementation strategies to both technical teams and executive stakeholders.

Evaluate third-party and partner-proposed AI solutions, assessing architecture, agent workflows, implementation quality, scalability, and alignment with enterprise standards.

Design and build enterprise GenAI applications using large language models, retrieval-augmented generation (RAG), embeddings, vector search, prompt engineering, and tool/function calling.

Architect and implement AI agents, multi-agent systems, and agentic workflows capable of executing complex, multi-step business processes.

Develop intelligent workflows that integrate LLMs with enterprise APIs, databases, business applications, and external tools.

Build solutions for unstructured data processing, including document and PDF extraction, OCR, image processing, classification, information retrieval, and summarization.

Develop and maintain high-quality Python services, API integrations, and reusable components that support reliable AI application development.

Leverage AI development frameworks such as LangChain, LangGraph, or equivalent technologies to implement orchestration, state management, and tool-enabled AI workflows.

Apply AI-assisted and specification-driven development practices to accelerate implementation while maintaining code quality, testability, and architectural consistency.

Design and implement AI solutions using enterprise data platforms, with familiarity across Databricks AI capabilities, including AI Functions, AI Search/Vector Search, Model Serving, and MLflow.

Integrate structured and unstructured enterprise data into AI applications while considering data quality, access controls, governance, and security requirements.

Define appropriate patterns for model selection, deployment, inference, retrieval, and integration with existing enterprise technology ecosystems.

Collaborate with data engineering, cloud engineering, and software development teams to ensure AI solutions integrate effectively with enterprise platforms and production systems.

Establish evaluation strategies to measure AI solution accuracy, relevance, groundedness, hallucination rates, tool-use effectiveness, latency, and cost.

Implement guardrails, responsible AI controls, human-in-the-loop workflows, and fallback mechanisms to support safe and reliable enterprise AI adoption.

Define testing and observability practices for monitoring agent execution, model behavior, workflow performance, and production issues.

Review solution designs and code for quality, maintainability, scalability, security, and operational readiness.

Identify opportunities to optimize model selection, prompt design, retrieval strategies, inference performance, and overall operating costs.

Support production deployment, troubleshooting, continuous improvement, and the transition of AI solutions into sustainable enterprise operations.

8+ years of overall experience in software engineering, solution architecture, AI/ML engineering, or related technical roles.

At least 2 years of hands-on experience developing Generative AI applications and LLM-powered solutions.

Demonstrated experience designing and building AI agents, agentic workflows, and multi-agent applications.

Strong practical knowledge of LLMs, RAG architectures, embeddings, vector databases or vector search, prompt engineering, and tool/function calling.

Experience processing unstructured data, including PDFs, documents, OCR outputs, images, and other enterprise content.

Strong Python development skills, including API development, integration patterns, debugging, and software engineering best practices.

Experience with AI application frameworks such as LangChain, LangGraph, or equivalent agent orchestration technologies.

Familiarity with Databricks AI capabilities, particularly AI Functions, AI Search/Vector Search, Model Serving, and MLflow.

Experience with AI evaluation frameworks, hallucination testing, guardrails, human-in-the-loop patterns, and AI observability.

Ability to assess AI architecture proposals, review code and agent workflows, and provide actionable technical recommendations.

Experience deploying AI applications into production environments, including considerations for scalability, security, monitoring, and cost optimization.

Strong communication and presentation skills, with the ability to explain technical concepts, trade-offs, and recommendations to enterprise clients.

Proven ability to work collaboratively with client stakeholders, technology partners, and multidisciplinary engineering teams.