Lead / Principal Forward Deployed Engineer - Control-M AI

BMC Software

1d•Remote

About The Position

As a Forward Deployed Engineer in Control-M Product Management, you will embed with a small set of strategic customers and design partners to deploy Control-M AI capabilities in real customer environments and bring back evidence that shapes product development. The role combines product management perspective with hands-on engineering: writing code, building workflows, standing up integrations, and driving adoption and customer outcomes. You will take Control-M AI capabilities from initial discovery through production use. Areas may include AI operations and remediation, AI-assisted workflow design, agentic orchestration, data orchestration and assurance, AI governance and enterprise controls, and integrations and Jobs-as-Code. You will work across customer environments including Control-M SaaS and self-hosted deployments, hybrid cloud, mainframe, ERP, and data platforms. Success is measured by production adoption, customer outcomes, and product improvements informed by field insights. You will also develop reusable assets and enable internal teams, partners, and customers to repeat successful deployments.

Requirements

8+ years of experience in software engineering, solutions or sales engineering, solution architecture, or technical consulting, including 3+ years working directly with enterprise customers.
Strong hands-on skills in Python or a similar language, REST APIs, JSON/YAML, and scripting, with the ability to build working software quickly and pragmatically.
Practical experience building with generative AI, including LLM APIs, prompt design, retrieval, tool calling, and agent frameworks, and sound judgment about appropriate AI use.
Knowledge of making AI useful, safe, and reliable in production.
Solid grounding in enterprise infrastructure, including AWS, Azure, or GCP; Linux and Windows; containers and Kubernetes; CI/CD; security; and identity.
Ability to lead discovery with technical and business stakeholders and turn ambiguous problems into working solutions and clear product requirements.
Strong product instincts and ability to turn one-off customer fixes into reusable capabilities.
Excellent written and verbal communication skills, including the ability to influence operators, architects, and executives.

Nice To Haves

Workload automation and orchestration experience; Control-M is strongly preferred, and other enterprise schedulers or Apache Airflow are also valuable.
Data engineering and modern data platform experience, such as Databricks, Snowflake, Spark, or dbt.
Experience with agentic AI frameworks, protocols, and platforms, such as LangGraph, Model Context Protocol, or Amazon Bedrock, including evaluation and guardrails.
Experience in IT operations, observability, SRE, or DevOps.
Experience with mainframe, SAP, or other enterprise application environments.
Experience in regulated industries such as financial services, insurance, healthcare, or the public sector.
Prior experience as a forward deployed engineer, solutions architect, or technical product manager at an enterprise software company.

Responsibilities

Embed with strategic Control-M customers and design partners to understand their workflows, operating models, and desired business outcomes.
Lead hands-on discovery workshops, identify valuable AI use cases, and agree on measurable success criteria.
Diagnose and remove technical, security, compliance, process, and trust-related barriers to AI adoption, moving customers from pilot to production.
Configure Control-M AI capabilities, define workflows as code, and build integrations and scripts, including with Python and REST APIs.
Design and implement agentic and LLM-powered workflows orchestrated by Control-M, including tool calling, retrieval, evaluation, and human-in-the-loop approvals.
Work across customer environments and harden prototypes with enterprise-grade security, auditability, and observability.
Translate customer observations into prioritized product requirements, user stories, and defect reports.
Partner with Product Managers, UX, and R&D to shape the Control-M AI roadmap and validate early releases with design partners.
Measure the impact of AI capabilities, including adoption, time saved, failures avoided, and time to resolution, and use the data to inform product priorities.
Create reusable reference architectures, workflow templates, prompt libraries, integration patterns, and adoption playbooks.
Enable Customer Success, Solution Engineering, Professional Services, and partners to repeat successful deployments at scale.
Share hands-on customer stories in briefings, executive meetings, analyst sessions, and events.