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.

Primary Outcomes

• 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.

Core Role Scope

1. AI Architecture and Technical Leadership

• 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.

2. Governance

• 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.

3. Engineering Team Leadership

• 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.

4. Client Solution and Delivery Ownership

• 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 technical depth for architects, developers, security teams, and platform owners.
• Lead discovery workshops, architecture reviews, technical demonstrations, pilot planning, and production-readiness decisions.
• Identify client dependencies, including platform access, licensing, data availability, security approval, integration readiness, and operating ownership.

5. Commercial Enablement

• Define baselines and success measures before development begins.
• Measure adoption, hours reclaimed, capacity released, demand absorbed, cycle-time reduction, quality improvement, cost avoidance, control effectiveness, and business outcomes.
• Distinguish between individual productivity tools, reusable service capabilities, process automation, and true digital workers.
• Partner with finance, account, and delivery leaders to create credible value cases and avoid inflated or duplicate benefit claims.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Information Systems, or a related technical discipline with demonstrated experience taking AI, automation, data, and intelligent workflow solutions from concept through production deployment and support.
• 10+ years of experience in software engineering, enterprise architecture, digital solutions, platform engineering, or technology consulting.
• 5+ years leading architects, engineers, developers, or multidisciplinary technical delivery teams.
• Experience building or materially scaling a technical practice, engineering capability, solution portfolio, or center of excellence, while serving the needs of customers in complex enterprise security, architecture, procurement, and governance requirements..
• Strong executive communication, with experience in client advisory services and negotiating financial and commercial terms.

Required Technical Depth

Candidates should demonstrate practical architecture and delivery experience across most of the following areas:

• Large language models and enterprise generative AI patterns.
• Agentic architectures, tool use, orchestration, workflow design, memory, and human-in-the-loop controls.
• Retrieval-augmented generation, enterprise search, embeddings, vector stores, knowledge graphs, and content grounding.
• Prompt and skill design, evaluation frameworks, testing methodologies, and reusable component development.
• API design, event-driven integration, enterprise application integration, and secure connectivity.
• Cloud-native architecture, containers, CI/CD, DevSecOps, infrastructure automation, and production monitoring.
• Data engineering, data governance, identity and access management, privacy, and security architecture.
• Microsoft Azure AI and Microsoft Copilot ecosystem experience. ServiceNow AI, automation, or workflow experience is strongly preferred.
• Application development using modern languages and frameworks, with the ability to challenge technical designs and guide implementation decisions.
• AI operations, including versioning, telemetry, quality monitoring, performance management, cost controls, incident response, and continuous improvement.