• Architect end-to-end integration solutions connecting internal AI co-worker platforms and enterprise data services with Salesforce Agentforce environments • Design scalable and secure distributed architectures across cloud-native enterprise ecosystems • Define integration blueprints using APIs, event-driven systems, and autonomous agent orchestration patterns • Design modular autonomous AI and micro-agent ecosystems to prevent overloaded reasoning engines • Establish governance standards for agent specialization, delegation boundaries, and semantic task routing • Evaluate and select optimal integration approaches, including REST APIs, gRPC services, event-driven architectures, and Model Context Protocol (MCP) • Build and integrate Salesforce-native AI capabilities using Invocable Apex methods, Advanced Salesforce Flows, and custom prompt templates • Enable intelligent workflows and autonomous execution capabilities within Salesforce ecosystems • Establish authentication boundaries, trust filters, and secure access controls for AI-driven enterprise workflows • Define data governance standards for sensitive enterprise information

Role Description We are currently seeking an AI Engineering Associate Director to join our team in Plano, Texas (US-TX), United States (US). We are seeking an experienced AI Engineer to design, build, test, and deploy artificial intelligence, machine learning, and generative AI solutions across industries. - Work with solution architects, industry SMEs, and client stakeholders to turn AI opportunities into working solutions that are scalable, secure, reusable, and aligned to business outcomes. - Hands-on engineer with strong experience across AI application development, data pipelines, model integration, cloud services, APIs, automation, and modern software engineering practices. - Contribute to client delivery, internal accelerators, reusable assets, proofs of concept, demos, and technical content that support both sales and implementation efforts. - Move from experimentation to production-minded engineering, balancing speed, technical quality, responsible AI practices, and business value. Location: Remote role to be located in the United States Key Responsibilities - Design, develop, and implement AI and generative AI solutions across primarily healthcare, life science, and public sector domains. - Build AI-enabled applications, services, APIs, copilots, chat interfaces, workflow automations, dashboards, and decision-support tools. - Develop solutions using modern AI engineering patterns, including retrieval-augmented generation, embeddings, vector search, prompt engineering, model orchestration, agentic workflows, natural language processing, document intelligence, predictive analytics, and machine learning. - Partner with solution architects, data scientists, cloud engineers, software developers, and business stakeholders to translate requirements into technical designs and working solutions. - Build and maintain data pipelines, integration services, feature preparation workflows, knowledge bases, and model-ready datasets. - Integrate AI models and services from major cloud providers, commercial model providers, open-source frameworks, and enterprise platforms. - Create proofs of concept, prototypes, demos, and technical assets that validate feasibility, demonstrate business value, and support client conversations. - Support the sales cycle by contributing to demo environments, technical walkthroughs, proposal content, estimates, and reusable solution components. - Develop reusable accelerators, code libraries, templates, prompt assets, deployment patterns, and implementation playbooks. - Apply responsible AI practices, including model evaluation, guardrails, privacy controls, bias testing, explainability, monitoring, auditability, and human-in-the-loop design. - Collaborate with security, privacy, legal, compliance, and architecture teams to ensure AI solutions meet enterprise and client requirements. - Participate in testing, debugging, performance tuning, deployment, documentation, and operational handoff. - Stay current on emerging AI technologies, frameworks, tools, and engineering practices, and translate relevant innovations into practical solutions. Qualifications - Bachelor’s degree in computer science, information systems, engineering, data science, analytics, or a related field, or equivalent practical experience. - 6+ years of experience in software engineering, data engineering, AI engineering, machine learning engineering, analytics engineering, or technical consulting. - Hands-on experience building AI, machine learning, generative AI, analytics, automation, or data-driven applications. - Practical experience with generative AI patterns such as prompt engineering, retrieval-augmented generation, embeddings, vector databases, LLM integration, model evaluation, and API-based model orchestration. - Proficiency in Python and SQL, with experience using common data, AI, and application development libraries or frameworks. - Experience with at least one major cloud platform such as Azure, AWS, or Google Cloud. - Experience working with APIs, databases, data pipelines, structured data, unstructured data, and enterprise integration patterns. - Familiarity with software engineering practices such as Git, CI/CD, testing, code review, documentation, containerization, and deployment. - Ability to build technically credible solutions while communicating clearly with both technical and non-technical audiences. - Strong collaboration skills and ability to work with cross-functional teams in consulting, client delivery, product, or enterprise technology environments. Required Qualifications - Bachelor’s degree in computer science, information systems, engineering, data science, analytics, or a related field, or equivalent practical experience. - 6+ years of experience in software engineering, data engineering, AI engineering, machine learning engineering, analytics engineering, or technical consulting. Benefits - Medical, dental, and vision insurance. - Flexible spending or health savings account. - Life and AD&D insurance. - Short and long term disability coverage. - Paid time off. - Employee assistance. - Participation in a 401k program with company match. - Additional voluntary or legally-required benefits.

• Lead the end-to-end lifecycle of AI-powered solutions, from opportunity assessment and business case development through deployment, monitoring, and continuous improvement. • Translate business needs into scalable AI-enabled workflows with clear KPIs, ROI models, success metrics, and operational standards. • Partner across product, engineering, data science, and operations teams to redesign processes, reduce manual effort, and improve customer and business outcomes. • Establish governance for model performance, compliance, responsible AI practices, and workflow quality to ensure reliable and ethical use of AI solutions. • Drive stakeholder alignment, adoption, training, and change management while fostering a culture of innovation, operational excellence, and continuous improvement.

• Design, build, and operate production-grade Generative AI solutions. • Work across data, models, and applications to deliver reliable AI workflows. • Own initiatives end-to-end, including architecture, experimentation, deployment, monitoring, and continuous improvement. • Shape how we apply Generative AI to high-value business problems by building systems that measurably improve customer experience, operational efficiency, and decision quality. • Raise the quality bar for production AI by setting standards for design, safety, evaluation, and operational readiness. • Mentor other engineers and partner with product, data science, and business stakeholders.