AI/ML Engineer (AI Platform & Backend Engineering)

Location:

Kothrud, Pune (On-site)

Experience:

2–4 Years

Employment Type:

Full-Time

Build AI Products. Own Your Work. Scale Intelligent Systems.

At SwayAlgo Technologies

, we build AI-powered products, enterprise platforms, intelligent web applications, and automation solutions.

Our engineers don't just integrate AI APIs—they build scalable AI platforms, solve complex engineering challenges, and create production-ready systems that power real-world applications.

We're looking for an AI/ML Engineer who combines strong AI engineering expertise with solid backend engineering skills. This role is AI/ML Engineering and Backend & Platform Engineering

, focused on building reliable, scalable, and cost-efficient AI systems.

If you enjoy working with the latest AI models, designing production AI infrastructure, and building end-to-end intelligent systems, we'd love to meet you.

What You'll Build

You'll contribute to a variety of AI-powered product initiatives, including:

- AI-powered applications
- Enterprise AI solutions
- Intelligent automation systems
- Multimodal AI applications
- AI APIs and backend services
- Scalable AI platforms and infrastructure

You'll work closely with frontend, backend, DevOps, and product teams to build production-ready AI systems used by real customers. Key Responsibilities AI Engineering Generative AI

- Build production-ready applications using Large Language Models (LLMs).
- Work with image generation and video generation AI models.
- Build multimodal AI workflows combining text, images, documents, and structured data.
- Design AI processing pipelines covering preprocessing, inference, post-processing, and response generation.
- Implement prompt engineering, prompt optimization, prompt versioning, and prompt caching.
- Integrate multiple AI providers and foundation models.

AI Platform Engineering Design and build the core AI platform powering production AI workloads.

Responsibilities include

- AI runtime routing across multiple providers and models
- Model selection based on latency, quality, availability, and cost
- AI request orchestration
- Prompt caching and semantic caching
- AI response caching
- Token consumption tracking
- AI usage analytics
- Cost calculation and optimization
- Budget monitoring and quota management
- Retry and fallback strategies
- AI provider failover
- AI evaluation pipelines
- Automated response scoring
- AI quality benchmarking
- Model comparison and A/B testing
- AI observability, logging, tracing, and monitoring

AI Performance & Scalability

- Design AI services capable of handling high-concurrency production workloads.
- Optimize inference latency, throughput, reliability, and infrastructure costs.
- Implement batching, streaming, asynchronous inference, and background processing.
- Build scalable AI services using queues, caching, and distributed architectures.
- Continuously optimize model performance and operational costs.

Backend & Platform Engineering Backend Engineering

- Build scalable backend services using Python , FastAPI

, and gRPC

.

- Design modular microservices and production-ready APIs.
- Build secure authentication, authorization, and API security.




- Develop event-driven and asynchronous processing systems.

- Write clean, maintainable, and production-quality code.

Data Engineering

- Design scalable PostgreSQL database schemas.
- Optimize SQL queries, indexing, and transactions.

- Build Redis-based caching layers.
- Implement queue mechanisms, distributed locking, and background workers.
- Develop efficient preprocessing and ingestion pipelines.

Infrastructure & DevOps

- Containerize AI services using Docker.
- Build and maintain CI/CD pipelines.
- Deploy AI workloads on cloud infrastructure.
- Monitor production systems using logs, metrics, and tracing.

- Design highly available, fault-tolerant, and scalable AI infrastructure.
- Optimize compute, GPU utilization, and cloud costs.

Product Ownership

- Understand business problems before designing AI solutions.

- Participate in architecture and technical design discussions.
- Build AI features end-to-end from design and development to deployment and monitoring.
- Take ownership of scalability, reliability, security, quality, and operational costs.

- Continuously improve AI systems based on performance metrics and customer feedback.

Required Skills AI & Generative AI Strong hands-on experience with:

- Large Language Models (LLMs)
- Prompt Engineering
- Prompt Optimization
- Prompt Caching
- AI Processing Pipelines
- Image Generation Models
- Video Generation Models
- Multimodal AI
- AI Evaluation
- AI Runtime Optimization
- Token Consumption Analysis
- Cost Optimization
- AI Provider Integration

AI Platforms Hands-on experience with one or more:

- Google Vertex AI
- AWS Bedrock
- Azure AI Foundry

Experience building vendor-agnostic AI applications across multiple providers is highly preferred.

Backend Engineering

Strong experience with

- Python
- FastAPI
- gRPC
- REST APIs
- Microservices
- Distributed Systems
- Asynchronous Programming
- PostgreSQL
- Redis
- Queue Mechanisms
- Caching Strategies
- Docker

Infrastructure & DevOps

Experience with

- Docker
- Git
- Linux
- CI/CD
- Cloud Platforms (AWS, GCP, Azure)
- Monitoring & Observability
- Production Deployments

Good understanding of:
- Kubernetes
- High-Concurrency Systems
- Load Balancing
- Rate Limiting
- Fault Tolerance
- Distributed Architectures
- Performance Optimization

Software Engineering

Strong understanding of

- Clean Architecture
- Design Patterns
- System Design
- API Design
- Debugging
- Performance Profiling
- Logging
- Testing
- Secure Coding Practices

Preferred Skills Experience with any of the following is a strong advantage:

- LangGraph
- LangChain
- LlamaIndex
- Model Context Protocol (MCP)
- AI Agents
- Workflow Orchestration
- Vector Databases (PGVector, Milvus, Pinecone, Weaviate)
- RabbitMQ, Kafka, NATS, Celery, BullMQ
- WebSockets
- GraphQL
- Object Storage (S3, GCS)
- Prometheus
- Grafana
- OpenTelemetry
- Kubernetes
- OCR
- Computer Vision
- Document AI

Open-source contributions,



AI research, production AI systems, or personal AI products are highly valued.

Who We're Looking For

We're looking for engineers who:

- Have a product mindset and think beyond model integration.
- Are strong AI engineers with solid backend engineering skills.
- Understand how to build scalable, reliable, and cost-efficient AI platforms.
- Can optimize AI quality, latency, throughput, and operational costs.
- Are self-managed , proactive, and accountable.

- Can build features end-to-end with minimal supervision.
- Take ownership instead of waiting for instructions.
- Enjoy solving complex engineering and AI infrastructure challenges.
- Thrive in a quick-paced startup environment.
- Want to become part of a long-term core engineering team .

Why Join SwayAlgo?

You'll work on

- AI-powered Applications
- Enterprise AI Solutions
- Generative AI Systems
- Multimodal AI Applications

- AI Platform Engineering
- Scalable AI Infrastructure
- Modern Backend Architecture
- End-to-End Product Development

You'll work directly with founders and experienced engineers, contribute to platform architecture, and build AI systems that power real-world products.

Our Engineering Culture

We value engineers who

- Think like product builders.
- Own problems, not just tasks.
- Build scalable systems with long-term thinking.

- Continuously learn and experiment with emerging AI technologies.
- Balance AI quality, latency, scalability, reliability, and cost.
- Collaborate openly and support teammates.
- Use AI responsibly while maintaining strong engineering fundamentals.

- Take pride in building production-ready products with real-world impact.

Who Should Apply

You'll thrive if you

- Love building AI products—not just AI demos.

- Enjoy backend engineering and AI infrastructure.
- Like solving scalability and performance challenges.
- Want ownership and technical freedom.
- Are looking for long-term growth in a product-focused startup.

Who Should Not Apply This role isn't for you if you:

- Only have experience calling AI APIs without building production systems.
- Prefer research-only work without shipping products.
- Avoid ownership or production responsibility.
- Are uncomfortable with ambiguity or rapid iteration.
- Are looking for a short-term role instead of helping build a company.

Hiring Process

1. Resume Screening
2. Screening Interview (Technical + Cultural Fit)
3. Technical Interview
4. HR Discussion
5. Offer

How to Apply If you're passionate about building scalable AI systems, modern backend platforms, and production-ready AI applications, we'd love to hear from you.

Send your application to:

[email protected]

Please include

- Resume (PDF)
- GitHub Profile
- Portfolio or AI Projects (if available)
- LinkedIn Profile

Email Subject: Application – AI/ML Engineer – Your Name

Join Our Core Team

At SwayAlgo Technologies

, we're building more than AI features—we're building scalable AI products and platforms for the future.

If you're passionate about Generative AI, AI Platform Engineering, backend systems, scalability, and building products end-to-end

, and want to grow as part of a long-term core engineering team

, we'd love to build the future with you.

📌 AI/ML Engineer (AI Platform & Backend Engineering) (Pune)
🏢 Swayalgo Technologies
📍 Pune