Home Based Ai/ml engineer Jobs – 208 Open Jobs Today

AI & ML Intern
Zeqo Systems —Remote
- ₹25,000 - ₹50,000 a month
- Part-time
- Remote
- Master's degree
- APIs
- AI
- Flexible schedule
Quick apply
AI Engineer - FDE (Forward Deployed Engineer)
Databricks —Remote
- Remote
- Azure
- Computer Science
- AWS
10d
Applied AI Engineer (Automation)
Fusemachines —Remote
- Full-time
- Remote
- Azure
- Master's degree
- Databases
AI/ML Developer
Fiddle Leaf Solutions —Remote
- Up to ₹1,50,000 a month
- Full-time
- Remote
- Computer Science
- AWS
- Bachelor's degree
- Work from home
Quick apply
Senior QA Automation Engineer (Agentic AI)
Ipeople Infosysteams LLC —Remote
- ₹30,00,000 - ₹45,00,000 a year
- Full-time
- Remote
- Master's degree
- SQL
- APIs
Quick apply
4d
AI/ML Engineer/ 5 +years/ Remote
APPTESTIFY GLOBAL SERVICES PVT LTD —Remote
- ₹80,000 - ₹1,00,000 a month
- Remote
- Azure
- Doctoral degree
- Computer Science
Quick apply
13d
Senior Ai/Ml Engineer
Hiring Mavericks —Remote
- ₹17,00,000 - ₹25,00,000 a year
- Full-time
- Remote
- Azure
- Computer Science
- SQL
Quick apply
Lead Engineer - AI/ML
Prospus —Remote
- Email
- Remote
- Master's degree
- Databases
- Analysis skills
AI / LLM Integration Engineer
Codvo.ai —Remote
- Full-time
- Remote
- Master's degree
- Machine learning
- Continuous improvement
- Work from home
AI Engineer (Fresher)
AAPNA Infotech —Remote
- Remote
- Master's degree
- Bachelor's degree
- Machine learning
ML Research Engineer - Computer Vision
Entrupy —Remote
- Remote
- Computer science
- Master's degree
- C++
- Paid time off
- Paid sick time
Quick apply
6d
AI Deployment Engineer, Startups
OpenAI —Remote
- Full-time
- Remote
- JavaScript
- APIs
- AI
AI Product Analyst
Armino Technologies —Remote
- Email
- Remote
- Master's degree
- Bachelor's degree
- AI
Quick apply
Cybersecurity Engineer Principal
General Dynamics Information Technology —Remote
- ₹1,31,750 - ₹1,78,250 a year
- Remote
- Azure
- B.Sc
- AWS
- Paid time off
- Work from home
12d
Associate Full-Stack Engineer — Applied AI
Vedasva Systems Pvt Ltd —Remote
- ₹2,40,000 - ₹4,00,000 a year
- Full-time
- Remote
- Computer Science
- Databases
- SQL
- Cell phone reimbursement
- Work from home
Quick apply
6d
Gen AI Engineer
Rupa Career Solutions —Remote
- ₹4,56,646.56 - ₹7,58,087.79 a year
- Full-time
- Remote
- Azure
- Master's degree
- Databases
Quick apply
7d
Software Engineer
UDCYDE —Remote
- ₹8,00,000.00 - ₹12,83,506.98 a year
- Full-time
- Remote
- Azure
- Computer Science
- Java
- Health insurance
Quick apply
12d
Staff Software Engineer -ML/AI
Lookout —Remote
- Remote
- Computer Science
- B.Sc
- Master's degree
Quick apply
AI Application Engineer
Rearc —Remote
- Full-time
- Remote
- Master's degree
- AWS
- Machine learning
Quick apply
Agentic AI Engineer (AWS Tech Stack)
ZoftSolutions Pvt Ltd —Remote
- Full-time
- Remote
- Master's degree
- SQL
- AWS
- Health insurance
- Paid time off
Quick apply

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AI & ML Intern

Zeqo Systems
Remote

Quick apply

Job details

Student job | Part-time | Fresher | Full-time | Contractual / Temporary | Internship
₹25,000 - ₹50,000 a month

Benefits

Flexible schedule

Qualifications

Law
Master's degree
Back-end development
APIs
JSON
AI
Python

Full job description

ROLE: AI / ML + Backend Engineer ₹25–50K / month

3-6 months · Remote

WHAT YOU OWN

The intelligence layer: LLM pipelines, RAG, multi-agent systems, vector search, fine-tuning data preparation, and the backend that makes personalised learning work at scale. You build production systems, not research notebooks. Every AI decision you ship gets used to guide real students.

WHAT THE WORK ACTUALLY LOOKS LIKE

On any given week you could be:

▪ Building an agent that analyses quiz patterns and flags at-risk students before the exam happens, not after

▪ Moving a RAG pipeline from fixed-k retrieval to dynamic k with confidence-score gating, then running the benchmarks to prove it works

▪ Writing the context assembly layer that ranks, deduplicates, and compresses retrieved chunks before the LLM sees them

▪ Running a QLoRA fine-tune on a domain-specific dataset and evaluating whether it actually improves pedagogical correctness

▪ Logging every generation run with its prompt, retrieved context, and output so the system is fully auditable

In education, a hallucination is not a minor issue. You build with that in mind.

Python: primary language. Clean, modular, production-ready.
LLM APIs: Claude, Gemini. Prompt engineering, structured outputs, cost optimisation.
RAG foundations: vector embeddings, pgvector, semantic search, chunking strategies, retrieval pipelines from scratch.
Agent frameworks: multi-agent orchestration, tool use, LangChain or custom.
GCP: Vertex AI, Cloud Run, BigQuery. Not just local experiments.
Cost engineering: token tracking, caching, model routing. LLM cost per student matters.
Backend: FastAPI, async Python, database design for AI workloads.

RAG ENGINEERING & PERSONALISATION

Personalisation is mostly a retrieval problem. You will own the full RAG stack, not just wiring an API but engineering the retrieval layer that makes each student's experience actually adaptive:

RAG fine-tuning: fine-tune embedding models and re-rankers on domain-specific content (CBSE curriculum, question bank, concept graph) so retrieval understands pedagogy, not just keyword overlap
Chunk optimisation: figure out the right chunk size and overlap for each content type (lesson text, worked examples, MCQs, formula sheets); back every decision with precision/recall numbers
k-selection: dynamic k based on query complexity, confidence scores, and context window budget; know the precision-recall-latency tradeoff and make deliberate choices
Retrieval speed: fast enough for real-time student interactions; HNSW tuning, ANN vs exact search, query batching, embedding caching, async pipelines
Personalisation via retrieval: enrich queries with each student's learning history (weak concepts, error patterns, last-seen content) so retrieval surfaces what is contextually relevant, not just semantically close
Context assembly: rank, deduplicate, and compress retrieved chunks before the LLM sees them; handle lost-in-the-middle degradation
Retrieval evals: hit rate, MRR, NDCG harness; no index change ships without a benchmark

Most teams treat RAG as a solved problem. We do not. The choice between k=3 and k=7, sentence vs paragraph chunks, cosine vs BM25 hybrid directly decides whether a student gets the right explanation or a wrong one.

TRAINING DATA & FINE-TUNING

A core part of this role is building the training pipeline for our own models. You will:

Audit pipeline: audit educational simulations for pedagogical, UX, and logic failures; convert findings into structured JSON feedback payloads
SFT data prep: generate (prompt, chosen, rejected) training triplets in JSONL format for supervised fine-tuning
DPO data prep: write high-quality rejected outputs that represent plausible-but-wrong model behaviour; know the difference between a hallucinated fix and a precise one
RAG-aware fine-tuning: fine-tune models to consume retrieved context properly: cite, synthesise, flag gaps, not hallucinate over them
Fine-tuning: LoRA or QLoRA on open-source base models (Qwen, Mistral, LLaMA)
Eval harness: automated evals scoring model output on pedagogical correctness, code validity, fix precision
Data governance: log all generation runs with prompts, retrieved context, and outputs; full reproducibility

The training data you produce directly determines how good our autonomous simulation engineer becomes. Annotation quality matters as much as quantity.

NICE TO HAVE

▪ Fine-tuning experience: LoRA, QLoRA, or full fine-tune on open-source models

▪ Embedding model fine-tuning: BGE, E5, GTE for domain-specific retrieval

▪ Re-ranker experience: cross-encoders, Cohere Rerank, or custom

▪ Annotation pipelines or RLHF/DPO dataset experience

▪ Hybrid search: BM25 + dense retrieval, Reciprocal Rank Fusion

▪ Manim or Matplotlib for programmatic diagram generation

▪ GCP Vertex AI hands-on experience

▪ LLM feature shipped to real users in production

▪ HuggingFace Trainer, TRL, or Axolotl

You are Final or pre-final year at IITs, NITs, BITS, or any other good institute. You have built something with LLMs beyond a ChatGPT wrapper: RAG, agents, fine-tuning, or embeddings that ran in production. You know the difference between a demo that works and a system that scales cheaply. You understand that in education, the model being wrong has real consequences. You care about engineering, not just accuracy numbers.

Post 3 months: option to convert to full time is available depending solely on your performance.

Pay: ₹25,000.00 - ₹50,000.00 per month

Benefits:

Flexible schedule

Work Location: Remote

Quick apply

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ai/ml engineer jobs in home based

AI & ML Intern

AI Engineer - FDE (Forward Deployed Engineer)

Applied AI Engineer (Automation)

AI/ML Developer

Senior QA Automation Engineer (Agentic AI)

AI/ML Engineer/ 5 +years/ Remote

Senior Ai/Ml Engineer

Lead Engineer - AI/ML

AI / LLM Integration Engineer

AI Engineer (Fresher)

ML Research Engineer - Computer Vision

AI Deployment Engineer, Startups

AI Product Analyst

Cybersecurity Engineer Principal

Associate Full-Stack Engineer — Applied AI

Gen AI Engineer

Software Engineer

Staff Software Engineer -ML/AI

AI Application Engineer

Agentic AI Engineer (AWS Tech Stack)

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