Tech Lead AI Platform Engineering (Python)

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Tech Lead – AI Platform Engineering (Python)

Engineering Bangalore / Remote Full-Time 6–10 Years Experience

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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.

Experience

Required Skills & Qualifications

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, or 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

Git and GitHub workflows.

GitHub Actions or equivalent CI/CD platforms.

Docker and containerized application development.

Understanding of modern DevOps practices.

Leadership Expectations

We Are Looking For Someone Who

Leads through technical excellence rather than hierarchy.

Takes ownership of problems rather than waiting for direction.

Can convert ambiguous ideas into working software.

Makes sound engineering decisions supported by data and experience.

Is passionate about continuous learning and adopting new technologies.

Enjoys mentoring fellow engineers through collaboration and example.

Maintains high standards for software quality and engineering discipline.

Communicates technical ideas clearly with both technical and non-technical stakeholders.

Preferred Skills

Experience with any of the following would be an added advantage:

Retrieval-Augmented Generation (RAG)

MCP (Model Context Protocol)

Vector Databases

Kubernetes

Terraform

AI evaluation frameworks

Observability platforms

Event-driven architectures

Enterprise integration patterns

Why Join Aumne.AI

At Aumne.AI, you'll have the opportunity to build technology that fundamentally changes how enterprises design and operate customer service platforms.

Here, You'll

Work directly with the CTO and founders on core platform architecture.

Build cutting-edge AI-native software using modern engineering practices.

Solve challenging problems involving LLMs, Knowledge Graphs, Distributed Systems, and Autonomous AI Agents.

Influence technical direction, architecture, and engineering standards from an early stage.

Work in a collaborative environment where ownership, innovation, and execution are valued.

Help build a world-class engineering culture grounded in our GRACE values.

Our GRACE Values

Gratitude First

Rooted in Innovation

AI-Native Thinking

Customer-Focused

Evolve for Outcomes

What We Offer

Competitive Compensation Aligned With Experience.

Opportunity to work on cutting-edge AI technologies.

Direct collaboration with founders and technical leadership.

High ownership with meaningful technical impact.

Flexible and remote-friendly work environment.

Continuous learning and professional growth.

Opportunity to shape the future of AI-native enterprise software.

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