About KPI Partners:

KPI Partners is a data, analytics, and AI consultancy founded in 2006.

We have 600+ consultants and 300+ enterprise customers, headquartered in Oakland, California with delivery and go-to-market teams across India, Mexico, and the UK.

We build modern data platforms, analytics products, and production-grade AI systems on AWS, Azure, GCP, Databricks, and Snowflake.

Our clients are moving past chatbots and copilots.

They now want AI agents that can plan, decide and act across their systems, running enterprise-wide autonomous workflows with the right human oversight.

These roles lead that shift.

The Role:

You will be the technical face of our Agentic AI practice with customers.

You will shape and sell agentic solutions to enterprise clients: finding the business processes where autonomous agents can take over multi-step work, and turning them into sound architectures, roadmaps and delivery plans.

This is a deeply technical, customer-facing leadership role.

You do not need to write production code, but you must be able to go whiteboard-to-whiteboard with a client's chief architect on agent orchestration, tool access and guardrails, challenge a design, and earn the trust of both CXOs and engineering teams.

Generative AI is the foundation; agentic AI is the focus.

We want leaders who think in workflows and outcomes, not prompts and demos: how an agent fits into a real process, what it is allowed to do, how it is evaluated and governed, and how it scales from one use case to the enterprise.

Key Responsibilities:

- Lead discovery with client executives and process owners to find workflows ripe for autonomy, such as order-to-cash, claims, IT operations, customer service and procurement, and qualify feasibility, data and system readiness, risk and ROI.

- Design end-to-end agentic architectures: single- and multi-agent orchestration, planning and reasoning, tool and API access, memory and state, retrieval as the knowledge layer, human-in-the-loop checkpoints, and integration with ERP, CRM, ITSM and data platforms.

- Own the technical narrative in proposals, SOWs and orals: solution approach, autonomy levels, estimates, staffing, risks and assumptions.

- Run executive workshops, agentic envisioning sessions and proof-of-concept scoping; present to CIO, CDO, CTO and COO audiences.

- Advise on build vs. buy, agent frameworks (such as LangGraph, CrewAI, AutoGen and Semantic Kernel), agent platforms across Azure, AWS, Google Cloud, Databricks and Snowflake, interoperability standards (such as MCP and A2A), and model trade-offs on cost, latency, quality and privacy.

- Set reference architectures, reusable accelerators and best practices for agentic systems: agent design patterns, orchestration, tool governance, evaluation and AgentOps/LLMOps.

- Define how agents are made safe for the enterprise: identity and least-privilege access for agents, approval gates, audit trails, prompt-injection defences and failure recovery.

- Establish agent evaluation and observability standards: task success, trajectory and tool-call quality, hallucination and error rates, cost per transaction and latency.

- Review designs from delivery teams and step in on escalations where the technical approach is at risk.

- Stay current on the fast-moving agent, model and tooling landscape, and turn it into clear points of view for clients and internal teams.

- Provide technical oversight on agentic engagements so that what was sold is what gets built, on time and running in production.

- Move clients from isolated pilots to an enterprise-wide agentic roadmap, with a shared platform, governance model and pipeline of workflows to automate.

- Grow accounts by identifying follow-on automation opportunities from successful deployments.

- Partner with hyperscaler and ISV alliance teams on joint go-to-market, co-selling and funding programs.

- Hire, mentor and lead a team of agentic AI architects, AI engineers and solution leads.

- Create thought leadership: client-facing points of view on agentic AI, webinars, conference talks and production case studies.

Required Qualifications:

- 15+ years in technology, with 8+ years in data, analytics, ML or AI and 2-3+ years leading GenAI/LLM initiatives, including agentic or autonomous workflow solutions.

- Proven customer-facing solutioning. You have led discovery, designed solutions and won deals for enterprise clients, not only delivered them.

- Experience in a consulting, systems integrator, hyperscaler or ISV field role (for example principal architect, field CTO, solutions director or AI practice lead).

- Examples of agentic or GenAI solutions you shaped that reached production and automated multi-step business workflows, with measurable outcomes such as cycle time, cost or throughput.

- Experience owning or co-owning pre-sales pipeline, proposals and deal pricing in the USD 1M+ range.

- Agent fundamentals: reasoning and planning patterns, tool and function calling, memory and state management, autonomy levels and human-in-the-loop design.

- Multi-agent orchestration: supervisor and hierarchical patterns, hand-offs, agent-to-agent communication, and standards such as MCP and A2A.

- LLM foundations: prompting, fine-tuning vs. RAG, embeddings, context windows, tokens and cost.

- Production concerns: agent evaluation, guardrails, observability, error handling and recovery, AgentOps/LLMOps, latency and cost optimization.

- Enterprise context: process and workflow design, data architecture, APIs and integration, identity and access for agents, security, responsible AI and regulatory requirements.

- Ability to critically review code, architectures and estimates produced by engineering teams.

- Executive presence: comfortable presenting to C-level audiences and turning technical depth into business value.

- Strong written skills for proposals, architecture documents and points of view.

- Experience building and leading teams of architects or senior engineers.

- Bachelor's degree in Computer Science, Engineering or a related field; Master's preferred.

Preferred:

- Background in intelligent automation, RPA or BPM (such as UiPath, Automation Anywhere, Power Automate or ServiceNow) and experience evolving rule-based automation into agentic workflows.

- Experience embedding agents into enterprise applications (SAP, Oracle, Salesforce, ServiceNow, Workday) or analytics platforms.

- Hands-on engineering or data science background earlier in your career (Python, ML frameworks, cloud services).

- Certifications or deep partner experience with Microsoft, AWS, Google Cloud, Databricks or Snowflake AI and agent offerings.

- Domain depth in one or more industries such as financial services, healthcare and life sciences, manufacturing, retail or high tech.

- Familiarity with AI governance frameworks (NIST AI RMF, EU AI Act, ISO/IEC 42001).

- Public thought leadership: talks, publications or open-source contributions