Remote Senior windows engineer Jobs – 32 Open Jobs Today

Senior AI Engineer (Full-Stack)
Codvo.ai —Remote
- Full-time
- Remote
- Azure
- Master's degree
- Databases
4d
Cloud Identity Engineer L3
Atidan Technologies —Remote
- Remote
- Microsoft Access
- Azure
- Windows
Platform Engr Sr
CSG —Remote
- Email
- Remote
- Computer Science
- DevOps
- AWS
Senior Data Analytics Engineer
MariaDB plc —Remote
- Remote
- Databases
- SQL
- JavaScript
Quick apply
sr GCP Devops Engineer
XIT Solutions Inc. —Remote
- ₹6,49,948.55 - ₹21,16,633.81 a year
- Part-time
- Remote
- Computer Science
- DevOps
- Engineering
Quick apply
13d
Machine Learning Engineer / Data Scientist
Fusemachines —Remote
- Full-time
- Remote
- Azure
- Windows
- SQL
Senior Data Engineer (with AI/ML experience) India
IDT —Remote
- Full-time
- Remote
- Azure
- English
- Windows
Quick apply
Senior Database Administrator (DBA) – Cloud Migration
RSN GINFO Solutions —Remote
- ₹10 - ₹15 an hour
- Remote
- Databases
- SQL
- AWS
SQL Developer / Data Engineer with ETL, Python
Mango Sciencies —Remote
- ₹2,96,633.39 - ₹13,73,466.16 a year
- Full-time
- Remote
- Oracle
- English
- Databases
Quick apply
(US) Senior Lead – AI Engineering (LangGraph / Haystack / RAG Systems)
Codvo.ai —Remote
- Remote
- APIs
- Agile
- AI
Invent L3 Engineer -Retail Planning Support Specialist (Invent.ai / MerchPlan)
RSN GINFO Solutions —Remote
- Rotational shift
- Remote
- Merchandising
- SQL
- Analysis skills
4d

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Senior AI Engineer (Full-Stack)

Codvo.ai
Remote

Apply Now

Job details

Full-time
4 days ago

Qualifications

ArcGIS
CI/CD
Azure
Node.js
Law
React
D3.js
Full-stack development
Master's degree
Databases
AWS
Docker
JavaScript
PostgreSQL
Continuous integration
Redis
SDKs
Back-end development
REST
APIs
SQLite
JSON
AI
Figma
TypeScript
Communication skills
Python
GIS

Full job description

Employment Type: Full-Time
Location: Remote

About the Role

We are looking for a Senior AI Engineer with strong full-stack capabilities to join our engineering team. You will work across multiple client-facing projects that sit at the intersection of AI, data, and web—starting with a live production AI agent platform.

You should be comfortable owning features end-to-end, from LLM pipeline design through to a polished frontend experience.

What You'll Do

AI Agent Engineering

Design and build AI agent pipelines, including multi-node LangGraph graphs.
Implement intent routing, multi-turn conversational context, session state management, and tool integrations.
Develop multi-step reasoning pipelines and graph-based agent workflows.

RAG & Knowledge Systems

Build and maintain Retrieval-Augmented Generation (RAG) systems.
Design vector search architectures, embedding pipelines, retrieval grounding, and chunking strategies.
Implement hallucination mitigation techniques and retrieval evaluation frameworks.

LLM Integration & Optimization

Integrate and optimize Large Language Models (LLMs) including OpenAI, Gemini, and Anthropic.
Develop structured output workflows using JSON schemas.
Create effective prompt engineering strategies, few-shot examples, and context window management solutions.
Build provider-neutral client architectures to support multiple LLM vendors.

Full-Stack Development

Design, develop, and deploy end-to-end product features.
Build scalable FastAPI backends and React/Next.js frontends.
Implement Server-Sent Events (SSE) streaming and REST API contracts.
Deliver production-ready UI features independently without requiring dedicated frontend support.

Observability & Quality

Own LLM observability including:
- Token usage logging
- Cost tracking
- Fallback detection
- Performance monitoring
- Regression test suites
Build evaluation pipelines and golden test suites to ensure AI quality and consistency.

Client & Product Collaboration

Collaborate directly with clients and stakeholders to understand business requirements.
Translate requirements into scalable, maintainable software solutions.
Keep technical documentation, specifications, and test coverage aligned with product changes.

Must-Have Skills

AI Agent Engineering

LangGraph or equivalent graph-based agent frameworks.
Multi-step reasoning pipelines.
Tool usage and orchestration.
State management and conversational workflows.

RAG & Vector Search

End-to-end RAG pipeline design and implementation.
Experience with vector databases such as:
- Pinecone
- Qdrant
- pgvector
- Weaviate
Chunking strategies and retrieval optimization.
Retrieval evaluation methodologies.

LLM Integration

OpenAI, Gemini, and Anthropic SDKs.
Prompt engineering and prompt optimization.
Structured JSON outputs.
Context window management.
Multi-provider LLM integrations.

Python Backend Development

Python 3.12
FastAPI
Async Python
Pydantic
SQLite
PostgreSQL
Redis
Pytest

Full-Stack Development

React
Next.js
TypeScript
Modern frontend architecture
API integration and state management

ML Engineering Fundamentals

Evaluation pipelines
Golden datasets and test suites
Regression tracking
Model performance monitoring

Good to Have

GIS & Mapping

ArcGIS REST APIs
GeoJSON
MapLibre GL JS
Spatial queries

(Strong advantage for initial project assignments.)

Data Visualization

Recharts
D3.js
Equivalent charting libraries

Cloud & DevOps

Docker
Azure
AWS
CI/CD pipelines
OIDC Authentication

Product Thinking

Ability to understand and interpret Figma designs.
Evaluate trade-offs between engineering effort and business value.
Deliver solutions aligned with business objectives.

Technologies You'll Work With

Layer

Technology Stack

Agent Frameworks

LangGraph, LangChain

LLM Providers

Gemini, OpenAI, Anthropic

Backend

Python 3.12, FastAPI, SQLite, Redis

Frontend

Next.js 15, React 19, TypeScript, Zustand

Data & Visualization

Recharts, GeoJSON, MapLibre GL JS

Infrastructure

Docker, Azure Pipelines, Azure AD

What We're Looking For

Someone who can independently own a feature from requirements gathering to production deployment.
Strong full-stack engineering capabilities with no hand-holding required between backend and frontend development.
Strong engineering judgment and the ability to push back when shortcuts introduce hallucination risks, reliability issues, or technical debt.
Comfortable working in ambiguous environments with evolving client requirements.
Experience delivering software in real-world production environments.
Excellent communication skills with the ability to explain AI system behavior and limitations to non-technical stakeholders.
Strong documentation and testing discipline.

Nice to Have (Domain Experience)

Experience in any of the following industries is a significant advantage, though not required:

Energy
Oil & Gas
Infrastructure
Enterprise GIS

Apply Now

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About the Role

What You'll Do

AI Agent Engineering

RAG & Knowledge Systems

LLM Integration & Optimization

Full-Stack Development

Observability & Quality

Client & Product Collaboration

Must-Have Skills

AI Agent Engineering

RAG & Vector Search

LLM Integration

Python Backend Development

Full-Stack Development

ML Engineering Fundamentals

Good to Have

GIS & Mapping

Data Visualization

Cloud & DevOps

Product Thinking

Technologies You'll Work With

What We're Looking For

Nice to Have (Domain Experience)