Apply for AI Engineer.
Tell us a little about you. We review every application and reply to those that look like a good fit.
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Location |
Bangalore, India (Hybrid) |
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Level |
Anyone between Mid to Senior level |
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Employment type |
Full-time, permanent |
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Practice |
REST APIs with FastAPI, Pydantic, and AWS (Bedrock, EC2, EMR, Lambda) |
Design and build RESTful AI APIs in FastAPI, implementing full CRUD (GET, POST, PUT, PATCH, DELETE) resources.
Define request/response schemas, validation, and serialization using Pydantic models.
Integrate AWS Bedrock foundation models and other LLM services behind clean, versioned API contracts.
Design async, non-blocking endpoints for high-throughput and streaming AI workloads.
Deploy and scale API services on AWS EC2 and Lambda, including auto-scaling and load-balancing configuration.
Build and orchestrate data-processing workflows on AWS EMR to support AI pipelines.
Implement authentication, authorization, rate-limiting, and API versioning (e.g. OAuth2, JWT, API keys).
Write automated tests (unit, integration, load) and maintain OpenAPI/Swagger documentation.
Implement caching, connection pooling, and performance tuning for scalable, low-latency APIs.
Set up CI/CD, logging, monitoring, and observability for API services in production.
Required Skills & Experience
Strong Python and backend software-engineering fundamentals.
Hands-on production experience building REST APIs with FastAPI, including full CRUD endpoint design.
Strong command of Pydantic for data validation, schema modelling, and serialization.
Hands-on experience with AWS Bedrock for generative AI / LLM integration.
Practical experience with AWS EC2, Lambda, and EMR for hosting and scaling API and data workloads.
Solid understanding of async Python (asyncio, async/await) for high-concurrency API design.
Experience with API security, authentication/authorization, and rate-limiting patterns.
Experience with cloud-native deployment, containerisation (Docker), and infrastructure-as-code.
Understanding of API scalability patterns: load balancing, caching, horizontal scaling, and queuing.
Desirable
Experience with GenAI frameworks (LangChain, LangGraph, CrewAI or similar) and RAG architectures.
Familiarity with vector databases (Pinecone, FAISS, pgvector).
API gateway experience (AWS API Gateway) and event-driven architectures (SQS, SNS, EventBridge).
MLOps / LLMOps tooling and model monitoring.
AWS certifications (AWS Certified AI Practitioner, Solutions Architect, or similar).
Responsible-AI / AI-governance experience.
What We Offer
Challenging, high-impact work across enterprise and government clients.
A collaborative team of experts and clear pathways for growth.
Support for professional certifications and continuous learning.
Flexible, hybrid working.
Competitive remuneration aligned to your experience and level.