Remote Ai/ml engineer / python Jobs – 157 Open Jobs Today

AI Engineer
Veltris —Remote
- Full-time
- Remote
- Master's degree
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- APIs
1h
Senior AI/ML Engineer
Spark Eighteen —Remote
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Staff Systems Engineer- Infrastructure (Employer of Record)
Credit Acceptance —Remote
- ₹34,17,563 - ₹50,12,426 a year
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Senior AIOps Engineer I
Porch —Remote
- ₹24,75,000 - ₹34,65,000 a year
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Senior AI Engineer I
AxisMaxlife —Remote
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Senior Data Engineer (with AI/ML experience) India
IDT —Remote
- Full-time
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AI Architech
Rupa Career Solutions —Remote
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- Microsoft Access
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11d
Freelance Software Engineers & Technology Consultants - 3+ Years | Remote Enterprise Projects |
Tatbac Technologies Pvt Ltd —Remote
- From ₹4,000 a day
- Remote
- Azure
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- Software troubleshooting
- Flexible schedule
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Data Engineer (ETL)
Technology Next —Remote
- ₹80,000 - ₹1,00,000 a month
- Full-time
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11d
Senior Ai ml Engineer
Refill Health Pvt . Ltd —Remote
- Part-time
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- Flexible schedule
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8d
Data Engineer - ETL
TECH NEXT —Remote
- ₹80,000 - ₹1,10,000 a month
- Full-time
- Remote
- Azure
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- Master's degree
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3d
Lead Gen AI/Agentic AI Engineer
Saransh —Remote
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6d
Domino Platform L3 Support / Development Engineer
RaiSha Globals —Remote
- From ₹50,000 a month
- Urgently hiring
- Remote
- Azure
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12d
Go Developer - AI & ML
Civo —Remote
- Remote
- Computer Science
- Bachelor's degree
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Senior Software Engineer IV - AI/ML
Avalara —Remote
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- Azure
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- Health insurance
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Machine Learning (ML) Engineer
Futops Technologies India Pvt. Ltd —Remote
- Full-time
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- Azure
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Backend Engineer [Mid-Level]
Portcast —Remote
- Full-time
- Remote
- Computer Science
- Master's degree
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GenAI / Cloud / Terraform Engineer
Delphic Global —Remote
- ₹3,02,403.10 - ₹19,88,528.38 a year
- Full-time
- Remote
- Azure
- Computer Science
- Software troubleshooting
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6d
AI Platform Engineer
AHEAD —Remote
- Remote
- Azure
- AWS
- Machine learning
- Health insurance
- Paid time off
AI Lead
Kasmoprav —Remote
- ₹400 - ₹550 an hour
- Email
- Remote
- Azure
- Research
- Master's degree
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AI Engineer

Veltris
Remote

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Job details

Full-time
1 hour ago

Qualifications

CI/CD
Go
Law
Kubernetes
Google Cloud Platform
Master's degree
SQL
Docker
PostgreSQL
Neo4j
Continuous integration
New Relic
REST
APIs
JSON
GraphQL
AI
gRPC
Graph databases
Python

Full job description

Job Information

Job Code

VL/IN/CMD/CI4/2026/355

Work Experience

5 - 7 Years

Date Opened

02/10/2026

Industry

IT Services

Job Type

Full Time Employment

Remote Job

Job Description

This is a remote position.

Role: AI Engineer

Experience: 4–8 years

We are looking for a Senior AI Engineer who treats LLMs as an engineering substrate — someone who

builds production-grade Go services on Google Cloud that turn model output into structured, deterministic,

schema-valid data the rest of the system can trust. This is a hands-on individual-contributor role with

significant ownership over design and implementation.

Our AI/ML work spans several modules — some LLM-backed, some deterministic — and you may contribute

across them over time. We therefore value engineers who are adaptable and strong on fundamentals over

narrow specialists, and who can pick up a new problem space quickly.

Key Responsibilities

Build & integrate LLMs: Design and build Go services that integrate LLMs into production workflows —

with strict structured output, confidence handling, and deterministic fallbacks when the model is

unavailable or low-confidence.

Reliable agentic workflows: Build multi-step and agentic workflows that execute reasoning, handle

errors, and maintain state — treating retries, timeouts, rate limiting, and graceful degradation as firstclass concerns.

Structured & deterministic output: Enforce strict Data systems: Work across the team's data and messaging stack — graph, analytical, and event-driven

stores — modeling data and writing efficient queries.

Evaluation & reliability: Define and own evaluation for AI components — datasets, regression/eval

harnesses, and metrics for accuracy, latency, cost, and reliability — so prompt and model changes ship

safely.

Production engineering: Ship multi-tenant, observable services on GCP that meet the team's coding

standard, and review peers' work to the same bar.Mandatory Skills & Qualifications

Go (Golang), production-grade: Strong, idiomatic Go — concurrency (goroutines, channels, context),

disciplined error handling, and clean, testable service code. You have shipped and maintained Go

backend services in production.

Applied LLM engineering: Hands-on experience integrating LLMs into production systems — prompt

design, structured/JSON output, function/tool calling, confidence handling, and fallback strategies. A

framework-agnostic grasp of agentic patterns (tool use, multi-step reasoning, state) and why reliability

matters more than cleverness.

GCP & Vertex AI: Practical experience on Google Cloud, ideally with Vertex AI (Gemini) and common data

and eventing services.

System-engineering mindset: You approach AI as an engineering problem — idempotency, retries, rate

limiting, timeouts, structured I/O, and graceful degradation rather than just prompt tuning. You design

for observabiData systems: Comfortable with SQL and at least one of: analytical (e.g. BigQuery), graph (e.g. Neo4j /

Cypher), or relational (e.g. PostgreSQL) stores. You can model data and write efficient queries.

APIs & services: You build clean service interfaces (gRPC / REST / GraphQL) and understand how to

expose backend logic as well-bounded “tools” that AI components can call safely.

Optional (But Highly Valued) Skills

Python: For prototyping, evaluation tooling, data work, or ML experimentation alongside the primary Go

stack.

Agent orchestration frameworks: Experience with agent / LLM-orchestration frameworks (e.g. Firebase

Genkit) or comparable tooling.

Knowledge graphs: Graph modeling, GraphRAG, or relationship inference at scale on graph databases.

Time-series & ML: Forecasting (e.g. ARIMA and related methods), BigQuery ML, or applied model

evaluation.

LLM security: Awareness of the OWASP Top 10 for LLM Applications (prompt/query injection, insecure

output handling, excessive agency), particularly where model output drives queries or actions.

Containerization & delivery: Docker, Kubernetes (GKE), and CI/CD.

Cost & latency optimization: Caching, batching, and model-tier selection to keep AI workloads efficient

at scale.

Tech Stack & Standards

(Experience in these or similar technologies is preferred)

Language: Go (primary); Python a plus.

AI / LLM: Vertex AI Gemini; agent-orchestration frameworks (e.g. Firebase Genkit).

Data & messaging: Graph (e.g. Neo4j / Cypher), analytical (e.g. BigQuery / BigQuery ML), object storage

and event streaming (e.g. Cloud Storage, Pub/Sub).

Cloud & deployment: Google Cloud Platform; containers and Kubernetes (GKE).

Engineering standards: Multi-tenant isolation, structured error handling, and automated evaluation for

AI components.

Observability: New Relic or equivalent.

Apply Now

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ai/ml engineer / python jobs in remote

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