Forward Deployed Engineer (Voice AI / Telephony) - Government
Posted 23 days ago
The role in short
Design, build, and deploy production voice AI agents and backend integrations for enterprise clients, taking full technical ownership of agent performance.
Job Title: Forward Deployed Engineer
Experience: 5-7 Years
About the Company:
Blue Machines builds production Voice AI for India's largest enterprises—handling millions of live conversations in Hindi, Hinglish, and regional languages. As a Forward Deployed Engineer, you will design, build, integrate, support, and debug production-grade AI agents end-to-end. You are directly responsible for the delivery and execution of your agent, not a partner handing off to it. You will bridge the gap between client systems and the Blue Machines platform across APIs, cloud services, and AI frameworks.
The Role
We are seeking a Forward Deployed Engineer (FDE) who bridges the gap between deep software engineering, cutting edge AI agent orchestration, and client delivery. In this role, you aren't just writing specifications or handing off prototypes you directly design, build, deploy, optimize, and debug production AI agents end-to-end. You will embed deeply with client environments to build seamless integrations while taking full technical ownership of agent performance in the wild.
What You'll Do
● End-to-End AI Agent Architecture: Design, prompt, build, and deploy production voice agents, including guardrails, conversation design, and tool-calling logic.
● Production Integration Engineering: Build scalable backend integrations (REST APIs, WebSockets, webhooks, callbacks, CRM writes) connecting the Blue Machines platform with enterprise systems.
● AI Orchestration & Ecosystems: Implement AI/LLM frameworks using LangChain, OpenAI APIs, or similar SDKs to deliver intelligent workflows.
● System Observability & Root-Cause Analysis: Read system logs, traces, and call flows to run rigorous root-cause analysis—proving whether an issue stems from prompt design, platform layer, or data pipelines.
● Agent Evaluation & Funnel Optimization: Continuously monitor and optimize agent metrics (completion rate, hallucination rate, latency, cost) and run funnel experiments on conversation cadence.
● Client Technical Leadership: Partner directly with client engineering teams, lead technical workshops, manage expectations with clarity, and travel for live-call reviews.
● Extensive Travel:The role is based out of Bangalore and requires extensive travel across various states in India, depending on project and business requirements.
What We Look For
● Core Experience: (5-7) years of software development experience, with at least 2+ years of hands-on experience shipping live LLM/AI applications (agents, RAG, or chatbots; voice experience is a plus).
● Core Backend Skills: Proficiency in Python or Java for backend architecture and integration work.
● Cloud & Web Architecture: Hands-on experience with cloud platforms (preferably GCP) along with deep knowledge of APIs, WebSockets, and auth protocols (OAuth2, JWT).
● Data Fluency: Comfortable pulling and analyzing data independently using SQL, Python, or Sheets.
● Communication & Presence: Strong client-facing communication skills with the ability to explain technical complexities in plain terms.
● Mobility: Willingness to travel for on-site client workshops and deployment reviews.
Nice to Have
● Knowledge of telephony stacks and real-time protocols (SIP, WebRTC, Twilio, Avaya, Ubona).
● Experience with Voice AI or Conversational AI Systems
Prep for this interview
5 questions likely to come up at Blue Machines AI, each matched to a line in this posting.
- 1Integrations and APIs
They wrote: “● Production Integration Engineering: Build scalable backend integrations (REST APIs, WebSockets, webhooks, callbacks, CRM writes) connecting the Blue Machines platform with enterprise systems.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 2LLMs, RAG and agents
They wrote: “● End-to-End AI Agent Architecture: Design, prompt, build, and deploy production voice agents, including guardrails, conversation design, and tool-calling logic.”
The model gives a confident wrong answer to an important customer. What now?
How to answer
Why they ask: Tests how you handle trust, on top of the technical fix.
- Tell the customer the same day what you know and when they'll hear more
- Reproduce it and find the cause (bad retrieval, missing data, prompt, model)
- Add the case to the test set so it can't come back unnoticed
- Add a guard where it matters: citations, confidence checks, human review
Tip: Own it with the customer first, then show the change that stops it happening again.
- 3Coding
They wrote: “● Core Backend Skills: Proficiency in Python or Java for backend architecture and integration work.”
Here's a CSV export from a customer. Write a script that cleans it and loads it into our API.
How to answer
Why they ask: Very close to real FDE work. They watch how you handle bad rows and failures.
- Read with a proper CSV parser; check the header first
- Validate each row and collect errors, not crash on the first one
- Send in batches with retries and a clear report at the end
- Write one or two quick tests for the parsing
Tip: Talk through edge cases out loud: encodings, missing fields, duplicates, rate limits.
- 4Data and SQL
They wrote: “● System Observability & Root-Cause Analysis: Read system logs, traces, and call flows to run rigorous root-cause analysis—proving whether an issue stems from prompt design, platform layer, or data pipelines.”
How would you design a daily pipeline that feeds customer data into our product?
How to answer
Why they ask: Tests basic data engineering: scheduling, failures, re-runs and monitoring.
- Extract with timestamps or change tracking, not full dumps, when possible
- Transform in steps you can re-run without creating duplicates
- Validate row counts and key fields before loading
- Alert a person on failure, with enough detail to fix it
Tip: Make re-running safe (same input, same result) and say how you'd know it failed.
- 5Cloud, deployment and security
They wrote: “● Cloud & Web Architecture: Hands-on experience with cloud platforms (preferably GCP) along with deep knowledge of APIs, WebSockets, and auth protocols (OAuth2, JWT).”
How would you debug a deployment that works in staging but fails at the customer's site?
How to answer
Why they ask: Customer environments differ in ways you can't see. This tests method under pressure.
- Compare versions, config and environment variables
- Check network rules, proxies and certificates (common in enterprise)
- Look at resource limits and permissions
- Add the missing check to your install steps afterwards
Tip: List the usual suspects and how you'd check each without full access.
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