Remote Ai agent developer Jobs – 87 Open Jobs Today

Principal Backend Engineer
Kairahire Solutions —Remote
- ₹30,00,000 - ₹50,00,000 a year
- Full-time
- Remote
- Master's degree
- AWS
- APIs
- Health insurance
- Work from home
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2h
Principal Engineer, Python fullstack (React+Genai)
Nagarro —Remote
- Full-time
- Remote
- Azure
- Computer Science
- System design
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12d
Senior Product Manager
Netomi —Remote
- Full-time
- Remote
- Computer Science
- Customer service
- Master's degree
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Full Stack Developer (RoR+React)-8+ Years
Trustfinity Service and Solution Pvt. Ltd. —Remote
- ₹50,00,000 - ₹60,00,000 a year
- Full-time
- Remote
- Computer Science
- SQL
- AWS
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3d
Senior .Net Core Developer
3Pillar —Remote
- Full-time
- Remote
- Azure
- APIs
- Agile
6d
Full Stack Developer(Python+React)-8+ Years
Trustfinity Service and Solution Pvt. Ltd. —Remote
- ₹50,00,000 - ₹60,00,000 a year
- Full-time
- Remote
- Computer Science
- SQL
- AWS
Quick apply
3d
Senior Python Engineer
Ciklum —Remote
- Full-time
- Remote
- Java
- Master's degree
- AWS
- Health insurance
7d

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Principal Backend Engineer

Kairahire Solutions
Remote

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Job details

Permanent | Full-time
₹30,00,000 - ₹50,00,000 a year
2 hours ago

Benefits

Internet reimbursement
Provident Fund
Health insurance
Work from home
Paid sick time

Qualifications

Law
Kubernetes
Software deployment
GD&T
Master's degree
Microservices
RBAC
AWS
Docker
PostgreSQL
Distributed systems
Back-end development
APIs
S3
SaaS
AI
RDS database
Python
Identity & access management

Full job description

ROLE OVERVIEW
We are seeking a Principal Backend Engineer to own the scalability, reliability, and architectural evolution of an AI platform as it transitions from a prototype to a production-grade platform. This is a high-impact, high-ownership role at the intersection of distributed systems engineering and applied AI.KEY RESPONSIBILITIES

Platform Architecture & Scalability: Own end-to-end backend architecture for the AI platform; design for multi-tenant, high-throughput extraction workloads
Extraction Pipeline Engineering: Architect and evolve AI pipelines, define pipeline abstractions, registry patterns, and execution strategies that balance accuracy, latency, and LLM API cost.
Multi-Provider LLM Abstraction: Enhance and harden the unified LLM provider layer ; implement multi-key round-robin pooling, structured output schemas, tool-calling protocols, and provider failover logic.
Async Worker & Queue Architecture: Design the distributed worker system for heavy pipeline execution observable queue with dead-letter handling, retry policies, and backpressure management.
AWS Production Deployment: Lead the AWS deployment architecture (ECS/EC2, RDS PostgreSQL, S3, Bedrock, ALB, Secrets Manager, CloudWatch); define IaC, blue-green deployment strategies, and ensure the platform meets security, compliance, and data residency requirements.
API Design & Governance: Define and enforce API design standards (OpenAPI 3.1, spec-first, versioning, deprecation); own the 25+ FastAPI endpoints, request/response schema evolution, and backward compatibility guarantees.
Data Architecture: Own the PostgreSQL schema for extraction metadata (holes, dimensions, GD&T, title blocks, notes, extraction traces, batches); design indexing strategies, multi-tenant isolation, and efficient querying patterns for the document review and dataset export workflows.
Security & Compliance: Implement secure service-to-service communication, secrets management via AWS Secrets Manager, IAM role-based access for Bedrock and S3, and CORS/auth policies
Observability & Reliability: Instrument the platform with structured logging, distributed tracing (AWS X-Ray / OpenTelemetry), and CloudWatch alarms; define SLOs for pipeline throughput, LLM call latency, and extraction accuracy.
Engineering Leadership: Mentor senior engineers, conduct architecture and design reviews, set coding standards, and drive the technical roadmap for the enterprise.

REQUIRED SKILLS & EXPERIENCE

10+ years of backend engineering experience with a strong focus on distributed systems and production-grade platform design.
Expert-level Python proficiency; deep hands-on experience with FastAPI, SQLAlchemy, Pydantic v2, and async/concurrent programming (asyncio, ThreadPoolExecutor).
Proven experience designing and operating microservices at scale — including service decomposition, inter-service communication, and failure isolation strategies.
Strong PostgreSQL expertise: schema design, indexing, query optimization, multi-tenant isolation (RLS or schema-per-tenant), and migration management.
Hands-on AWS production experience: EC2/ECS, RDS, S3, ALB, IAM, Secrets Manager, CloudWatch, and ideally Amazon Bedrock.
Deep understanding of event-driven and async architectures: message queues, polling workers, idempotency guarantees, retry strategies, and backpressure handling.
Experience integrating LLM APIs (any of: Anthropic Claude, OpenAI, Google Gemini, Mistral, AWS Bedrock) in production — including rate limiting, structured output enforcement, and multi-provider failover.
Experience with container-based deployments using Docker and Docker Compose; familiarity with ECS task definitions and service orchestration.
Strong API lifecycle management skills: OpenAPI 3.1, versioning, backward compatibility, deprecation policies, and governance frameworks.
Practical knowledge of distributed system patterns: sagas, circuit breakers, idempotency keys, at-least-once delivery, and graceful degradation.
Security engineering fundamentals: OAuth2, JWT, RBAC, CORS, secrets management, and cloud IAM role design.
Prior experience building developer platforms, internal tooling, or AI/ML serving platforms at large-scale organizations.

PREFERRED / GOOD TO HAVE

Direct experience building AI/LLM extraction pipelines — structured output, tool-calling, multi-agent orchestration (LangGraph, CrewAI, or custom frameworks).
Familiarity with agentic pipeline patterns: planner-executor swarms, multi-round tool-use loops, shared notepad patterns, and confidence-based validation.
Experience with Amazon Bedrock cross-region inference and IAM-based model access (Amazon Nova, Claude via Bedrock).
Knowledge of ML model serving infrastructure: HuggingFace Transformers, PyTorch model loading, YOLO/object detection integration, ChromaDB vector stores.
Experience with PDF processing pipelines: rasterization (PyMuPDF), coordinate normalization, multi-page extraction, and bounding box annotation workflows.
Familiarity with service mesh (Istio/Linkerd), Kubernetes, or EKS for cloud-native deployment evolution.
Experience with workflow orchestration frameworks (Temporal, Airflow, or equivalent) for long-running, stateful extraction jobs.
Experience in high-scale SaaS platforms with strict SLAs, multi-region deployments, and enterprise security requirements.

Pay: ₹3,000,000.00 - ₹5,000,000.00 per year

Benefits:

Health insurance
Internet reimbursement
Paid sick time
Provident Fund
Work from home

Work Location: Remote

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