Lead / Principal Forward Deployed Engineer - Control-M AI

Job description

BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage. Control-M orchestrates the application, data, and AI workflows that the world’s largest enterprises depend on, and we are building generative and agentic AI into every stage of that lifecycle — designing, building, running, troubleshooting, and remediating workflows. Shipping AI capabilities is only half the job. The other half is making them work, and stick, at customers who run their most critical business processes on Control-M. As a Forward Deployed Engineer (FDE) in Control-M Product Management, you will embed with a small set of strategic customers and design partners, get Control-M’s AI capabilities running in their real environments, and bring back the evidence that shapes what we build next. You will write code, build workflows, and stand up integrations. Your success will be measured on adoption, on customer outcomes, and on the product changes your insights drive. Think of it as a product manager with an engineer’s toolkit. You will go deep with a few lighthouse customers, working side by side with the product managers, architects, and R&D leaders who own the Control-M roadmap. What You’ll Work On Depending on customer priorities and your strengths, you will take Control-M AI capabilities from first conversation to production use in areas such as: AI operations & remediation — the Jett AI assistant, AI Execution Insights, and AI-driven troubleshooting and root-cause analysis AI-assisted workflow design — natural-language workflow creation with AI Workflow Creator and AI Pilot for Planning Agentic orchestration — orchestrating AI agents and LLM pipelines (for example, LangGraph and Amazon Bedrock AgentCore) alongside the application and data workflows customers already run Data orchestration & assurance — reliable pipelines across modern data platforms, with Data Assurance validating data quality as it moves AI governance & enterprise controls — guardrails, auditability, access control, and human-in-the-loop approvals Integrations & Jobs-as-Code — the Control-M Automation API, CI/CD pipelines, and custom integrations with customer systems Here is how, through this role, you will contribute to BMC’s and your own success: Customer Discovery & AI Adoption Embed with strategic Control-M customers and design partners to understand their workflows, operating models, and the business outcomes that matter most. Run hands-on discovery workshops to identify high-value AI use cases, and agree on measurable success criteria before work starts. Diagnose what is holding back AI adoption — technical gaps, security and compliance concerns, process friction, or trust — remove those blockers, and take customers from pilot to production. Build & Deploy Prototype and deliver working solutions fast: configure Control-M AI capabilities, define workflows as code, and write the integrations and scripts (Python, REST APIs) that connect them to customer systems. 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 real customer environments — Control-M SaaS and self-hosted, hybrid cloud, mainframe, ERP, and data platforms — and harden prototypes with enterprise-grade security, auditability, and observability. Product Feedback & Roadmap Influence Be the voice of the field inside Product Management for AI capabilities: turn what you see at customers into clear, prioritized 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 ahead of general availability. Measure the impact of AI capabilities — adoption, time saved, failures avoided, time to resolution — and use the data to sharpen product priorities. Scale & Enablement Codify what works into reusable assets: 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. Bring credible, hands-on stories from the field to customer briefings, executive meetings, analyst sessions, and events. What Success Looks Like Production adoption: Control-M AI capabilities move from pilot to everyday production use at the customers you support. This is the northstar for this role. Faster time to value: Customers go from first workshop to a working, measurable AI use case in weeks, not quarters. Product impact: Your field insights turn into shipped product improvements, new integrations, and better out-of-the-box experiences. Reuse at scale: Your templates, patterns, and playbooks are picked up by other teams, partners, and customers. Trusted advisor: Customers ask for you by name when they want to put AI to work in mission-critical operations. To ensure you’re set up for success, you will bring the following skillset & experience: 8+ years 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; you build working software quickly and pragmatically. Practical experience building with generative AI — LLM APIs, prompt design, retrieval, tool calling, and agent frameworks — and sound judgment about where AI does and does not belong. You don’t need to be an ML researcher; you do need to know how to make AI useful, safe, and reliable in production. Solid grounding in enterprise infrastructure: AWS, Azure, or GCP; Linux and Windows; containers and Kubernetes; CI/CD; security and identity. Proven ability to lead discovery with technical and business stakeholders and turn ambiguous problems into working solutions and clear product requirements. Strong product instincts: you naturally ask “should this be in the product?” and can turn a one-off customer fix into a reusable capability. Excellent written and verbal communication skills, with the ability to influence operators, architects, and executives alike. Whilst these are nice to have, our team can help you develop in the following skills: Workload automation and orchestration — Control-M strongly preferred; other enterprise schedulers or Apache Airflow also valuable Data engineering and modern data platforms (e.g., Databricks, Snowflake, Spark, dbt) Agentic AI frameworks, protocols, and platforms (e.g., LangGraph, Model Context Protocol, Amazon Bedrock), including evaluation and guardrails IT operations, observability, SRE, or DevOps Mainframe, SAP, or other enterprise application environments 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 #LI-Remote CA-DNP Our commitment to you! BMC’s culture is built around its people. We have 6000+ brilliant minds working together across the globe. You won’t be known just by your employee number, but for your true authentic self. BMC lets you be YOU! If after reading the above, You’re unsure if you meet the qualifications of this role but are deeply excited about BMC and this team, we still encourage you to apply! We want to attract talents from diverse backgrounds and experience to ensure we face the world together with the best ideas! BMC is committed to equal opportunity employment regardless of race, age, sex, creed, color, religion, citizenship status, sexua