What are the responsibilities and job description for the Director – Agentic AI Transformation position at Galent?
We have an immediate opening for a Director – Agentic AI Transformation with a leading IT service/solutions provider in Houston, TX.
Director – Agentic AI Transformation
Location: Houston,TX. Hybrid, 2-3 days onsite per week
JD:
The Forward Deployed Context Engineering Lead (Director) is responsible for designing, deploying, and governing end-to-end context and retrieval architectures that power generative AI and agentic AI solutions for strategic financial services clients. Embedded at client sites using a embedded forward deployment model, this leader translates business imperatives into production-grade AI deployments, context pipelines, tool orchestration frameworks, and evaluation systems that ensure AI workflows are compliant, reliable, and measurable against regulatory and business standards.
In highly regulated financial services environments, this role bridges AI innovation with governance frameworks including BCBS 239 (risk data aggregation), NIST AI RMF, ISO 42001, and emerging AI regulations. The holder serves as both a trusted technical advisor and strategic business partner, shaping how AI augments operations.
Key Responsibilities
Strategic Partnership & Client Leadership
- Partner with CFO, CIO, COO, Chief Risk Officer, and Chief Compliance Officer to define multi-year AI transformation roadmaps, with emphasis on use case prioritization, governance alignment, and risk-adjusted ROI.
- Work "forward deployed" at client sites, embedding within business units and technology teams to own end-to-end solution delivery from discovery through production and value realization.
- Lead discovery workshops to uncover high-value AI opportunities, frame problem statements, and shape outcome-driven implementations in core business processes (credit decisioning, market risk, AML/CFT, wealth advisory).
End-to-End Context Architecture
- Own the complete context fabric that feeds LLMs and agents: data products, RAG pipelines, vector databases, knowledge graphs, semantic layers, tool schemas, memory stores, and orchestration patterns.
- Design and oversee implementation of enterprise-scale retrieval systems that integrate multiple data sources (core banking, risk repositories, regulatory data, market data, customer data warehouses) with sub-second latency and high recall.
- Architect tool landscapes for agents, defining function schemas, validation rules, pre/post-execution guardrails, and escalation patterns so agents safely interact with core systems (core banking APIs, trading platforms, CRM, regulatory reporting engines).
- Establish context quality and freshness standards aligned to use case sensitivity: real-time for trading contexts, hourly for compliance contexts, daily for advisory contexts.
AI & Data Governance & BCBS 239 Alignment
- Embed data lineage, quality controls, and metadata management into context pipelines to satisfy BCBS 239 principles (completeness, accuracy, timeliness, clarity, granularity) and emerging AI data governance expectations.
- Work with Chief Data Officer and data governance teams to ensure context data products meet regulatory lineage requirements, audit trails, and change management protocols.
- Design data product contracts that codify context completeness, freshness, and accuracy SLAs and make them machine-readable for automated quality gates.
**AI Evaluation & Safety Strategy**
- Define and operationalize comprehensive evaluation strategy covering accuracy, consistency, hallucination detection, bias, fairness, latency, cost, and regulatory compliance by use case.
- Establish baseline and continuous metrics for both offline benchmarking (held-out test sets, red teaming) and online monitoring (production feedback, human review, alert escalations).
- Partner with Evaluation Engineering and Risk teams to implement automated quality gates in CI/CD pipelines, blocking unsafe or regressing models/prompts/context changes from deployment.
- Lead design and execution of red team exercises for high-risk use cases (credit decisioning, investment advice, transaction monitoring), including jailbreak detection, prompt injection, data leakage, and discriminatory output testing.
Experience & Qualifications
Required
- 15 years of experience across software engineering, data engineering, data science, or AI/analytics, with at least 5–7 years leading AI/ML transformation initiatives.
- Proven track record leading large-scale AI or digital transformation programs at consulting firms (Deloitte, PwC, Accenture, Cognizant) or equivalent director/senior manager roles in financial services technology.
- Demonstrated expertise working in a forward-deployed or embedded model, owning end-to-end solution delivery from architecture through production launch and ongoing optimization.
- Hands-on technical expertise in modern AI stacks: LLMs, RAG, vector databases, cloud platforms, and ML engineering practices.
- Prior experience in financial services (investment banking, capital markets, wealth management, payments, insurance) or other regulated domains (healthcare, government).
- Strong communication skills: ability to translate technical AI concepts for C-suite audiences and facilitate workshops with business and risk stakeholders.