As the Solution Delivery Director (AI: Intelligent Automation, Advanced Analytics, Generative AI), you will own end-to-end delivery of AI-driven solutions, spanning discovery, design, model development, engineering, deployment and stabilization, across intelligent automation, advanced analytics, and generative AI. You will lead cross-functional teams of data scientists, ML engineers, platform engineers, product owners, and change leaders to achieve business outcomes with strong governance, quality, risk control, and client satisfaction.

As a national premier consulting firm, alliant is focused on providing solutions to help businesses transform and thrive. alliant offers six different service lines to our clients and this role is within in the alliant Digital service line which helps businesses on their AI journey.  

Responsibilities:

• Shape and manage a portfolio of AI initiatives that balance quick wins (automation and analytics) with longer-horizon platform and generative AI capabilities, sequencing use cases by value, feasibility, risk, and data readiness
• Establish AI-specific stage gates (use-case intake, data readiness, model readiness, safety/compliance review, deployment, and post-deployment monitoring) and run disciplined governance rituals tailored to ML lifecycle and GenAI evaluation
• Translate use cases and SOWs into integrated delivery plans that account for experimentation cycles, model training time, evaluation loops, and integration work, with transparent change control that protects timelines and business value
• Serve as the senior delivery leader and trusted advisor to clients, framing AI opportunities and trade-offs in business terms, shaping success metrics (e.g., cost/time savings, revenue uplift, risk/quality improvements), and driving alignment through structured reviews
• Ensure solutions meet non-functional requirements for latency, scalability, reliability, and security; guide choices on architectures (data lakehouse, feature store, MLOps platforms, vector databases, RAG pipelines, orchestration frameworks) and integration patterns with enterprise systems
• Drive rigorous data profiling, lineage, access controls, and quality baselines; align with governance on PII/PHI handling, consent, retention, and purpose limitation, and ensure training/serving data management is auditable and compliant
• Lead teams to deliver robust models and agents with reproducible workflows, CI/CD for ML (feature pipelines, model packaging, automated tests), and deployment to target runtimes (batch, streaming, real-time APIs) with model registry and approval workflows
• Institutionalize red-teaming, prompt/guardrail strategies, refusal policies, and automated eval suites (toxicity, bias, hallucination, jailbreak resistance, factuality) with human-in-the-loop processes and content moderation where appropriate
• Define quality strategies that include unit/integration tests, data and feature tests, offline and online model evaluation, A/B or shadow testing, performance testing, and post-release SLOs with rollback and fallback mechanisms
• Embed responsible AI principles and regulatory requirements (e.g., AI Act, sector regulations) throughout the lifecycle; maintain model cards, decision logs, and audit trails, and ensure explainability, fairness assessments, and incident response readiness
• Grow and mentor a blended team of PMs, DS/ML engineers, platform engineers, solution architects, and automation specialists; set expectations for documentation, reproducibility, knowledge sharing, and iterate-with-evidence culture
• Ensure accurate estimating that accounts for experimentation and platform costs (compute, storage, model/API usage), manage budgets and cloud spend, and align change control to value realization while partnering with sales on credible AI proposals
• Coordinate SI partners and AI vendors (cloud AI services, LLM providers, RPA/iPaaS, vector DBs) with clear SLAs, usage policies, and versioning strategies; manage model/provider selection, contracts, and switching/contingency options
• Provide executive reporting on business outcomes and model health (drift, data quality, latency, cost per inference, error rates, user satisfaction), institutionalize post-implementation reviews, and feed learnings back into playbooks and templates.

Qualifications:

• Bachelor’s or Master’s degree required (in Business Administration, Information Technology, or related field)

• Proven experience (prefer 8 to 10 years) in solution delivery, professional services, or program management with substantial leadership of AI/ML, intelligent automation, analytics, and/or GenAI initiatives in production

• Strong technical fluency across modern AI stacks: cloud data platf