Job description
Engineering Manager – Forward Deployed Engineering (FDE) Location: Bangalore Experience: 8–12 Years Important Note This role is not for managers who have moved away from technology.
We expect our FDE Managers to remain deeply technical and capable of designing, building, debugging, and reviewing systems alongside their teams.
The strongest candidates for this role are equally comfortable: Leading engineers Designing architectures Debugging production issues Reviewing code Working directly with customers Driving execution in ambiguous environments About Blue Machines Blue Machines is building AI Employees.
We help enterprises deploy AI agents across customer support, sales, operations, research, collections, recruitment, and other business functions.
Our agents operate across voice, chat, email, CRM systems, internal tools, and enterprise workflows.
We are looking for an Engineering Manager to lead our Forward Deployed Engineering (FDE) team.
About The Role Forward Deployed Engineering sits at the intersection of Engineering, Product, AI, and Customer Success.
Unlike traditional engineering teams that primarily build platform capabilities, the FDE team works directly with enterprise customers to understand business problems, design solutions, build custom workflows, deploy AI agents, and drive measurable business outcomes.
You will lead a team responsible for taking customers from: "We want to automate this workflow" to "The AI employee is live, measurable, and delivering business value.
" This is a highly hands-on leadership role.
You will be expected to switch seamlessly between: Customer discussions Solution architecture Engineering reviews Production debugging Delivery planning Team leadership No two customer deployments look the same.
Success in this role requires strong technical judgment, adaptability, and ownership.
What You'll Do Lead the Forward Deployed Engineering Team Manage and mentor a team of Forward Deployed Engineers.
Establish a high-performance engineering culture.
Drive execution across multiple customer deployments.
Help engineers navigate ambiguity and technical complexity.
Participate in hiring and team building.
Create an environment of accountability, ownership, and continuous improvement.
Own Customer Success Through Engineering Work directly with enterprise customers to: Understand business workflows.
Identify automation opportunities.
Translate requirements into technical solutions.
Design AI-powered workflows.
Lead customer deployments from design to production.
Ensure successful adoption and measurable outcomes.
You will regularly engage with: CTOs Engineering leaders Product leaders Operations teams Customer support organizations Business stakeholders Architect AI Solutions Design and review solutions involving: Voice AI Agent orchestration Workflow automation Enterprise integrations Knowledge retrieval systems CRM integrations Telephony platforms Customer support systems You should be comfortable moving from business requirements to technical architecture with minimal guidance.
Remain Deeply Hands-On.
This is not a purely managerial position.
You should be capable of: Reviewing production code.
Conducting architecture reviews.
Debugging production incidents.
Building prototypes.
Helping engineers solve complex technical problems.
Evaluating engineering tradeoffs.
Identifying reliability and scalability risks.
While you may not write production code every day, you should be capable of personally building and debugging systems when required.
Drive Execution Balance customer commitments with engineering capacity.
Prioritize effectively across multiple deployments.
Identify delivery risks early.
Manage scope and stakeholder expectations.
Ensure predictable execution and delivery quality.
Improve Operational Excellence Lead production incident reviews.
Improve observability and debugging practices.
Drive reliability improvements.
Establish strong engineering operational processes.
Create feedback loops that improve delivery quality over time.
What Success Looks Like Within 3 Months Build strong relationships with customers and engineering teams.
Understand the Blue Machines platform and deployment patterns.
Contribute meaningfully to customer solution design.
Establish credibility as a technical leader.
Within 6 Months Successfully lead multiple customer deployments.
Improve engineering quality and delivery predictability.
Help scale FDE processes and practices.
Mentor engineers through complex customer implementations.
Within 12 Months Become a trusted leader for customers and internal teams.
Scale the FDE organization while maintaining engineering quality.
Drive strategic customer deployments.
Help define how AI Employees are deployed across industries.
What We're Looking For Required 8+ years of software engineering experience.
2+ years of engineering management or technical leadership experience.
Strong hands-on engineering background.
Experience designing and operating production systems.
Strong system design and architecture skills.
Strong debugging and troubleshooting capability.
Experience leading engineers through complex technical projects.
Excellent communication skills.
Strongly Preferred Experience in one or more of: AI Agents LLM Applications Voice AI Conversational AI Contact Center Technologies Workflow Automation Enterprise SaaS Platforms Customer-Facing Engineering Solutions Engineering Distributed Systems Traits We Value We value leaders who: Take ownership.
Think from first principles.
Move quickly while maintaining quality.
Stay calm during production incidents.
Communicate clearly.
Make pragmatic decisions.
Build trust with customers and engineers.
Thrive in ambiguity.
Most importantly, we value leaders who can move seamlessly between customer conversations, architecture reviews, debugging sessions, engineering execution, and team leadership.
Interview Process Our process is designed to evaluate real-world engineering leadership capability.
Stage 1: Take Home Assignment Build a Voice AI Banking Customer Support Agent.
We evaluate: Engineering quality Architecture Product thinking Documentation Ownership Round 1: Assignment Deep Dive & Technical Review Discussion of: Architecture decisions Implementation choices Tradeoffs Scalability Product decisions Round 2: Production Debugging & Incident Management Realistic production scenarios involving: Logs Metrics Reliability issues Customer escalations Incident response Round 3: Engineering Leadership & Execution Scenarios covering: Prioritization Capacity planning Customer commitments Team leadership Technical decision making Why Join Blue Machines Work at the frontier of AI and Voice AI.
Solve meaningful business problems using AI Employees.
Work directly with enterprise customers.
Lead high-impact deployments.
Influence architecture, product direction, and engineering culture.
Help define the future of AI-powered work.
If you enjoy solving hard problems, building AI systems, leading engineers, and working closely with customers, we'd love to hear from you.
This job post has been translated by AI and may contain minor differences or errors.
