Senior Software Engineer Techlead

Join Us at Aumne.ai
Aumne.AI is building the next generation of AI-native Customer Service Lifecycle Management (SLM).
Our mission is to transform how enterprises design, build, migrate, and manage customer service applications by leveraging Artificial Intelligence, Large Language Models, Knowledge Graphs, and Autonomous Agents.
Our flagship platform, A.C.T (Agentic Conversation Transformer System), automatically transforms legacy customer service assets—including IVR applications, VXML, flow diagrams, business documents, APIs, and conversational logic—into modern AI-powered voice and chat experiences.
Instead of manually building customer service solutions, we are creating an intelligent platform that understands, generates, and continuously evolves enterprise conversational systems.
If solving complex engineering problems using modern AI technologies excites you, we'd love to have you join us.
Job Title: Tech Lead – AI Platform Engineering (Python)
Experience: 6–10 Years
Location: Bangalore / Remote
Department: Engineering
Employment Type: Full-Time
Role Summary
We are looking for a highly capable Tech Lead – AI Platform Engineering to work directly with the CTO in designing, building, and evolving the core technology powering Aumne.AI.
This is a hands-on technical leadership role, not a people management position.
You will lead by example through architecture, engineering excellence, and technical execution while contributing production-quality code across our backend and AI platform.
You should enjoy solving difficult engineering problems, working across multiple technologies, making architectural decisions, mentoring engineers, and taking complete ownership of technical outcomes.
You will play a key role in building an AI-native platform that combines distributed backend systems, graph databases, large language models, agent orchestration, cloud-native services, and enterprise-grade automation.
What You’ll Build
As part of the engineering leadership team, you will contribute to building and evolving:
AI Agent orchestration and workflow engines
Knowledge Graph and graph-based reasoning platform using Neo4j
Backend platform powering the A.C.T System
LLM-powered transformation engines
Enterprise API and microservice platform
Cloud-native distributed services
Autonomous Service Lifecycle Management (SLM) capabilities
Enterprise conversational transformation platform
Scalable developer platform and engineering framework
Core Responsibilities
Technical Leadership
Work directly with the CTO to drive technical strategy and engineering excellence.
Lead technical design and architecture discussions for new platform capabilities.
Translate business requirements into scalable technical solutions.
Take ownership of technical decisions and implementation strategy.
Establish engineering best practices, coding standards, and design principles.
Participate in technical hiring and evaluation of engineering candidates.
Architecture & System Design
Design scalable backend architecture supporting AI-native workflows.
Design reliable distributed systems capable of handling enterprise-scale workloads.
Make architecture decisions balancing scalability, maintainability, performance, security, and operational simplicity.
Design reusable platform components and shared engineering libraries.
Drive API-first architecture and service-oriented design principles.
Backend Engineering
Design and develop high-performance backend systems using Python.
Build REST APIs and microservices using FastAPI and Pydantic.
Develop asynchronous and concurrent backend services using AsyncIO, threading, and multiprocessing where appropriate.
Design efficient data models using PostgreSQL and Neo4j.
Implement caching strategies using Redis.
Build highly maintainable and testable software following clean architecture principles.
AI Platform Engineering
Build production-ready AI workflows using LangChain and LangGraph.
Design and integrate agentic workflows supporting autonomous task execution.
Build LLM-powered backend services using OpenAI, Vertex AI, or equivalent foundation models.
Design prompt orchestration and context management strategies.
Integrate AI components with graph databases and enterprise APIs.
Continuously improve AI workflow reliability, observability, and performance.
Engineering Excellence
Perform architecture reviews and code reviews.
Improve engineering productivity through reusable frameworks and automation.
Optimize application performance, scalability, and reliability.
Troubleshoot complex production issues and drive root cause analysis.
Champion engineering quality through testing, documentation, and continuous improvement.
Collaboration & Ownership
Work closely with Product, AI Engineering, QA, and the CTO to deliver high-quality platform capabilities.
Mentor engineers through technical guidance and code reviews.
Take complete ownership of features from design through production deployment.
Proactively identify technical risks and propose practical solutions.
Thrive in a fast-paced startup environment where adaptability and ownership are essential.
Required Skills & Qualifications
Experience
6–10 years of hands-on experience in backend software engineering.
Proven experience leading technical design and implementation of complex backend systems.
Experience working in product companies or technology-driven startups is preferred.
Backend Engineering
Strong expertise in Python with solid software engineering fundamentals.
Strong understanding of Object-Oriented Design and practical design patterns.
Extensive experience building REST APIs using FastAPI.
Strong knowledge of asynchronous programming using AsyncIO.
Experience with multithreading and multiprocessing.
Strong understanding of PostgreSQL.
Experience with Redis caching.
Experience building scalable microservices.
Good understanding of software performance optimization.
Strong debugging and analytical problem-solving skills.
AI Engineering (Mandatory)
Hands-on experience building production-oriented AI workflows using LangChain.
Hands-on experience designing workflow orchestration using LangGraph.
Experience integrating Large Language Models into production applications.
Practical understanding of Prompt Engineering and Context Engineering.
Experience building AI-powered backend services and agentic workflows.
Knowledge Graph (Mandatory)
Hands-on experience using Neo4j.
Strong understanding of Graph Data Modelling.
Experience writing and optimizing Cypher queries.
Experience integrating graph databases into backend or AI applications.
Cloud & Distributed Systems (Mandatory)
Strong hands-on experience with at least one major cloud platform:
AWS
GCP
Azure
Strong understanding of distributed backend systems.
Experience designing scalable service-to-service communication.
Knowledge of asynchronous messaging, caching strategies, resilience patterns, and system reliability.
Understanding of monitoring, logging, and production operations.
DevOps

Work arrangement
Yes