Practice Leader, AI Engineering & Digital Workforce Solutions

BCforward is seeking a leader to build and lead our AI Engineering and Digital Workforce Practice. This unique opportunity positions a technologist within the mandate of a senior management leader who will be accountable for translating business opportunities into secure, production-ready AI solutions and for leading the development teams that create agents, reusable AI skills, intelligent workflows, and digital workers.

This is not a research-only, advisory-only, or innovation-lab role. The leader must be technically credible with architects and engineers, capable of reviewing solution designs and code-level approaches, and experienced in directing multidisciplinary teams through architecture, development, testing, deployment, monitoring, and production support. This leader will enable BCforward practices to redesign services around governed human-plus-AI delivery models that improve capacity, speed, quality, control, and business outcomes.

This is an internal, full time opportunity based in Indianapolis, IN.

• Create a scalable AI engineering practice with clear architecture, engineering, security, deployment, and support standards.
• Build production-grade agents, reusable skills, digital workers, and AI-enabled solutions that can be embedded into BCforward services.
• Lead and grow technical delivery teams, including AI architects, AI engineers, full-stack engineers, data and knowledge engineers, integration specialists, and AI operations resources.
• Convert client use cases and service opportunities into prioritized roadmaps, pilots, production deployments, and repeatable offerings.
• Establish measurable value realization for AI-enabled delivery, including capacity released, demand absorbed, cost avoidance, delivery acceleration, quality, control, and risk reduction.
• Enable commercial models based on capabilities, services, and outcomes.

• Serve as BCforward's senior technical authority for enterprise AI, agentic solutions, digital workers, and AI-enabled service delivery.
• Define reference architectures, approved technology patterns, engineering standards, and reusable components for agent and skill development.
• Lead architecture decisions involving large language models, retrieval-augmented generation, knowledge stores, orchestration, integrations, APIs, user experiences, security controls, observability, and human oversight.
• Establish lifecycle standards covering intake, use-case definition, architecture, development, evaluation, testing, approval, deployment, versioning, monitoring, incident response, and retirement alongside engineering quality gates.
• Review solution designs, technical specifications, backlog decomposition, proof-of-concept results, and production-readiness evidence.
• Create reusable components and delivery accelerators that can be configured across engagements rather than rebuilt.
• Ensure solutions align with client-approved platforms, enterprise architecture, data controls, and deployment standards.

• Define governance for models, prompts, skills, agents, tools, data sources, actions, and digital workers, ensuring responsible use and permitted actions.
• Ensure every production solution has a named owner, defined task, measurable output, operating controls, support model, and auditable evidence within agent and skill registries with versioning, approved environments, and supporting documentation.
• Embed security, privacy, access control, data classification, auditability, human review, and responsible AI expectations into the engineering lifecycle.
• Ensure evaluation and monitoring cover accuracy, groundedness, reliability, bias risk, data leakage, unauthorized actions, performance, and cost.
• Establish incident management and rollback expectations for AI-enabled production services.

• Lead multidisciplinary AI delivery teams, who understand both the technical solution and the business process or service outcome being improved.
• Develop technical career paths, role definitions, competency expectations, and training plans.
• Create a scalable delivery model that can combine senior technical leadership with onshore, offshore, and partner capacity where appropriate.

• Work directly with client business, technology, security, procurement, and governance stakeholders to define AI opportunities and constraints, communicating credibly with executives while also providing sufficient…