Senior Backend & AI Engineer

Overview

About the Role

We're seeking a Senior Backend & AI Engineer to build and scale Inflx.ai - our AI-powered social media automation platform. This is primarily an individual contributor (IC) role with technical leadership responsibilities. You'll spend most of your time hands-on coding, building production-grade backend systems for conversational AI agents that automate social media interactions (DMs, comments, chats) across Instagram, Facebook, Twitter/X, LinkedIn, and websites. You'll work closely with our Data Scientist (who handles ML strategy and complex models), frontend engineer, and junior developers to deliver robust, scalable AI agent systems that handle millions of conversations 24/7.

About Inflx.ai: We build AI agents that automate social media conversations, turning followers into customers. Our agents handle DMs, comments, and chats across all major platforms - helping creators, brands, and businesses engage with their audience 24/7 while they sleep, grow, or focus on creating content.

Experience Level

5-8 years of professional experience in backend development and DevOps/cloud infrastructure

Key Responsibilities

Hands-On Development (Primary Focus - 70-80% of time)

• Write production-quality code daily in Python and Node.js/JavaScript
• Build and maintain backend services, APIs, and microservices for AI agents using MERN stack and Python frameworks
• Deploy and serve AI agents and ML models developed by the data science team
• Build infrastructure to support multi-agent systems, agent orchestration, and agent-to-agent communication
• Implement and maintain CI/CD pipelines using Jenkins, GitHub Actions, or similar tools
• Write and maintain Terraform scripts for infrastructure provisioning
• Manage Kubernetes clusters and container orchestration
• Debug and resolve complex technical issues across backend and infrastructure
• Conduct code reviews and ensure code quality standards
• Write comprehensive tests and maintain high code coverage

Technical Leadership (20-30% of time)

• Lead the technical direction and architecture of backend systems and cloud infrastructure
• Mentor junior engineers through code reviews, pair programming, and technical guidance
• Collaborate with the Data Scientist to integrate ML models into production systems
• Drive technical decision-making and establish best practices for backend development and DevOps
• Own the end-to-end technical roadmap for backend services and infrastructure

AI Agent & ML Model Integration

• Deploy conversational AI agents and ML models to production (developed by the data science team)
• Build backend infrastructure for multi-platform agent execution (Instagram, Facebook, Twitter/X, LinkedIn, web chat)
• Implement conversation state management, memory systems, and context handling for ongoing conversations
• Build APIs for social media platform integrations, webhooks, and real-time message processing
• Implement agent response generation, auto-reply systems, and conversation routing
• Build systems for agent monitoring, conversation analytics, and performance tracking
• Collaborate with the Data Scientist on conversational agent architecture and NLP integration
• Set up MLOps infrastructure for agents (versioning, A/B testing conversation flows, rollback capabilities)
• Implement agent orchestration to handle thousands of concurrent conversations across platforms

Backend Development

• Design and build scalable, high-performance backend systems and APIs using both Python and Node.js
• Develop production-grade services using MERN stack (MongoDB, Express.js, React.js, Node.js)
• Build RESTful and GraphQL APIs for social media integrations, webhooks, and real-time messaging
• Develop microservices architecture to support conversational AI agents and ML model serving
• Implement real-time message processing pipelines for handling DMs, comments, and chats at scale
• Build robust integration layers with social media APIs (Instagram Graph API, Facebook Messenger, Twitter API, LinkedIn API)
• Design and optimize database schemas for conversation history, user data, and agent configurations
• Implement caching strategies, rate limiting, and performance optimization for high-volume message processing
• Ensure system reliability, security, and performance at scale (handling millions of messages daily)
• Write clean, maintainable, and well-documented code following best practices

Cloud & Deployment

• Deploy and manage ML models and backend services on cloud platforms (AWS/GCP/Azure)
• Build and maintain CI/CD pipelines using Jenkins, GitHub Actions, or similar tools
• Write and maintain Terraform scripts for infrastructure provisioning and management
• Set up containerization with Docker and orchestration with Kubernetes
• Implement automated testing, deployment, and rollback strategies
• Configure and manage cloud services (load balancers, auto-scaling, networking, security groups)
• Monitor and optimize cloud resource utilization and costs
• Ensure high availability, disaster recovery, and backup strategies
• Manage secrets, environment variables, and configuration across environments

System Ownership

• Understand and own the complete system architecture from data ingestion to user-facing APIs
• Debug and resolve complex issues across the full stack
• Implement comprehensive testing strategies (unit, integration, end-to-end)
• Maintain technical documentation and system diagrams
• Drive performance optimization and scalability improvements

Required Qualifications

Must-Have Skills (Non-Negotiable)

Backend Development

• Python: Strong proficiency with frameworks like FastAPI, Flask, or Django
• Node.js/JavaScript: Solid experience with Express.js and building production-grade backend services
• MERN Stack: Hands-on experience with MongoDB, Express.js, React.js, and Node.js
• API Development: Expert-level understanding of RESTful API design and microservices architecture
• Production Systems: Proven track record of building, deploying, and maintaining production-grade backend services at scale
• System Design: Strong understanding of scalable architecture patterns, load balancing, caching strategies
• Databases: Proficiency with both SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis) databases

Cloud & Infrastructure

• Cloud Platforms: Strong hands-on experience with AWS, GCP, or Azure (EC2, S3, Lambda, RDS, VPC, IAM, etc.)
• Kubernetes: Solid experience with K8s for container orchestration, deployments, services, and scaling
• CI/CD: Hands-on experience building and maintaining pipelines with Jenkins, GitHub Actions, or GitLab CI
• Docker: Strong containerization skills for applications and services
• Monitoring & Logging: Experience with Prometheus, Grafana, ELK Stack, DataDog, or CloudWatch
• Version Control: Proficient with Git, GitHub/GitLab workflows, branching strategies, code reviews

AI Agents & ML Integration (Required)

• Conversational AI Understanding: Solid understanding of conversational AI agents, chatbots, LLMs, and NLP
• Agent Architecture: Familiarity with agent frameworks (LangChain, LlamaIndex, Rasa, Dialogflow, or similar)
• Social Media APIs: Understanding of social media platform APIs and webhooks (Instagram, Facebook, Twitter, LinkedIn)
• Model Deployment: Experience deploying and serving ML models and conversational agents in production
• Real-time Systems: Experience building real-time messaging systems and handling webhooks
• LLM Integration: Understanding of how to integrate LLMs (OpenAI, Anthropic, open-source models) for conversation generation
• Conversation Management: Familiarity with conversation state, context handling, and multi-turn dialogues
• Basic NLP: Understanding of NLP concepts, intent recognition, entity extraction, and sentiment analysis

Strong Plus (Highly Desired)

Advanced AI/ML Engineering

• Agent Development: Hands-on experience building AI agents from scratch
• Multi-Agent Systems: Experience with agent orchestration and multi-agent collaboration
• ML Model Training: Experience training and fine-tuning ML models or LLMs
• MLOps: Experience with ML pipelines, model versioning, and monitoring tools
• ML Frameworks: Hands-on experience with TensorFlow, PyTorch, or scikit-learn
• Vector Databases: Experience with vector stores (Pinecone, Weaviate, Chroma) for agent memory

ML/AI Expertise

• Experience training and fine-tuning machine learning models
• Understanding of ML model evaluation, validation, and monitoring
• Familiarity with LLMs, agents, and modern AI architectures
• Knowledge of MLOps tools and practices (MLflow, Weights & Biases, etc.)
• Experience with data preprocessing, feature engineering, and model optimization

Leadership & Soft Skills

• IC-First Mindset: Comfortable spending 70-80% of time coding and building
• Proven ability to lead technical projects from conception to deployment while remaining hands-on
• Experience mentoring junior engineers through code reviews, pair programming, and technical guidance
• Strong communication skills to collaborate with data scientists and translate ML requirements into backend services
• Self-motivated with strong ownership and accountability
• Problem-solving mindset with attention to detail
• Comfortable working independently and making technical decisions
• Ability to balance technical excellence with pragmatic delivery

Nice to Have

• Experience with TypeScript for type-safe backend development
• Familiarity with message queues (RabbitMQ, Kafka, AWS SQS) for high-volume message processing
• Experience with serverless architectures (AWS Lambda, Google Cloud Functions) for webhook handling
• Hands-on experience with Helm charts for Kubernetes deployments
• Experience with APM tools (New Relic, DataDog, Dynatrace)
• Knowledge of WebSockets and Server-Sent Events (SSE) for real-time conversation streaming
• Experience with load testing tools (JMeter, Locust, k6) for testing high-volume messaging systems
• Familiarity with security scanning tools and DevSecOps practices
• Experience with testing frameworks (Jest, Mocha, pytest) and TDD practices
• Knowledge of blue-green deployments, canary releases, and feature flags
• Contributions to open-source conversational AI, chatbot, or social media automation projects
• Experience working in social media, SaaS, or conversational AI product companies
• Understanding of prompt engineering, conversation design, and LLM optimization techniques
• Experience with OAuth flows and social media authentication

What Success Looks Like

• First 30 days: Understand the existing agent architecture, codebase, and team dynamics; deploy your first backend service or agent improvement
• First 90 days: Own the backend and infrastructure roadmap; establish CI/CD best practices; successfully deploy AI agents and ML models to production
• First 6 months: Lead the technical direction for agent backend systems; mentor junior engineers; drive measurable improvements in agent performance, reliability, and deployment velocity
• First year: Establish yourself as the technical authority for agent infrastructure; build a robust, scalable agent platform; create a culture of technical excellence

Why Join Us

• Work on Inflx.ai - a cutting-edge conversational AI platform automating millions of social media interactions
• Build the infrastructure that powers intelligent agents helping creators and brands engage 24/7
• Solve challenging problems at scale (real-time messaging, multi-platform integrations, high-volume processing)
• Lead technical initiatives and shape the future of social media automation
• Collaborate with talented engineers and an experienced Data Scientist
• Opportunity for rapid growth and increased responsibility in a fast-growing startup
• Be at the forefront of the AI agent revolution in social media
• Work on a product that's loved by creators, startups, and brands across the US & Europe
• Competitive compensation and benefits