Senior AI Engineer

Senior Engineer (Backend and Realtime Agentic AI Systems)

Location: Bengaluru (On-site)

Type: Full-time

Designation: Senior Engineer

About the Role

We are looking for a Senior Engineer who can architect, build, and scale complex backend systems, real-time audio/AI pipelines, and high-performance data infrastructure.

This role requires deep hands-on engineering expertise across Python backend services, WebSockets, real-time audio processing, SQL/Aurora optimization, AI model integrations, and AWS infrastructure.

You will work on mission-critical systems that power real-time voice bots, automated content workflows, multi-source ETL pipelines, and high-scale backend microservices. If you enjoy solving hard engineering problems end-to-end from designing system architecture to writing production-grade code this role is for you.

Key Responsibilities

1. Backend Engineering (Python / FastAPI / Microservices)

• Design, develop, and maintain scalable backend services using Python (FastAPI/asyncio).
• Build REST and WebSocket APIs with high reliability and low latency.
• Implement modular microservice architectures with clear separation of concerns.
• Own API design, versioning, documentation, and production deployment.

2. Real-Time Systems & Audio Infrastructure

• Build and optimize real-time audio streaming pipelines using WebSockets.
• Implement advanced voice interaction features
• Debug concurrency, frame loss, and performance bottlenecks in real-time systems.

3. AI/LLM Engineering (GPT Realtime, TTS, Transcription)

• Integrate and orchestrate LLMs for real-time conversational systems.
• Build tooling around GPT Realtime, transcription models, and TTS engines.
• Design AI workflows with LLMs and Agentic AI Systems
• Optimize AI system costs, latency, and reliability across large-scale usage.
• Develop guardrails and safety frameworks for the LLM use cases ( LLM Ops and Evals )

4. Data Engineering & Database Optimization

• Design and optimize MySQL/Aurora schemas for high-volume datasets.
• Implement partitioning strategies, indexing plans, and query optimizations.
• Build ETL workflows for analytics pipelines.
• Maintain data integrity and performance with replicas, backups, and migrations.

5. Cloud Infrastructure & DevOps (AWS)

• Deploy and maintain systems on AWS and GCP
• Manage CI/CD pipelines, containerization, and automated deployments.
• Implement observability: logging, metrics, alerts (CloudWatch, OpenTelemetry).
• Troubleshoot live production issues involving memory, CPU, concurrency, or networking.

6. System Architecture & Technical Leadership

• Drive architectural decisions for backend, audio, data, and AI systems.
• Establish engineering best practices testing, monitoring, coding standards.
• Lead root-cause analysis and long-term technical fixes.
• Collaborate closely with cross-functional teams (engineering, academic, ops).

Required Skills & Experience

Core Technical Skills

• 510+ years in backend engineering with Python
• Strong experience with:
• WebSocket-based systems
• Real-time streaming pipelines
• Audio processing fundamentals (PCM/WAV, VAD concepts)
• LLM and Agentic AI systems
• Deep understanding of relational DB internals MySQL/Aurora, indexing, query plans.
• Proven ability to design and maintain distributed systems on AWS.
• Strong understanding of cloud networking, Docker, ECS, IAM, and S3-based workflows.

AI / ML Integration Skills

• Experience integrating LLMs via APIs and agents (OpenAI, Whisper, GPT Realtime, Caude, Gemini, etc.).
• Strong understanding of TTS engines, transcription pipelines.
• Ability to build AI-powered backend features with multi-step orchestration.

Systems Engineering Skills

• Hands-on experience with concurrency (asyncio), multithreading, and performance tuning.
• Fluency with ffmpeg, audio transformations, and media workflows.
• Deep debugging skills across logs, traces, async event loops, and memory profiles.

Nice-to-Have Skills

• Experience with real-time communications (RTC), SIP, telephony, VoIP.
• Knowledge of event-driven patterns using Kafka/SQS.
• Exposure to analytics engineering (Looker Studio, Google Sheets APIs).
• Familiarity with content automation workflows (PDF, VTT processing, etc.).

What Success Looks Like

Within 36 months, you should be able to independently:

• Own and operate critical real-time audio + AI pipelines with agentic flows.
• Build and deploy high-quality backend services end-to-end.
• Maintain production stability, optimize performance, and reduce costs.
• Take architectural ownership of challenging engineering problems.
• Improve reliability, latency, and scalability of all core systems.

Why This Role Is Unique

Youll work on a cutting-edge blend of:

• Real-time communication systems
• LLM-driven engineering
• Large-scale data infrastructure
• High-performance backend systems
• Multi-modal AI + audio workflows

Few roles offer exposure across AI + systems engineering + backend + real-time infrastructure at this depth.