Tell an agent in March that you hold 200 units of a fund, tell it in June that you sold half, and a badly built one will still say 200 in August. Fixing that memory failure for good is the daily craft behind these remote agentic AI engineer jobs. The position is full-time, pays $125,000 annually (USD) and can be done from anywhere. You will build agents that remember users across sessions, connect them to tools through Model Context Protocol servers and test them in production. It suits software or ML engineers with roughly three years in the trade and a shipped agent of their own.
The project team has chat features that answer and forget. It now needs agents that hold a goal over many steps, recall what a user said weeks ago and fetch live data through tools.
Those pieces fail quietly. An agent with a slightly wrong memory still sounds confident, so the hiring team wants one engineer who owns memory and retrieval end to end and measures them with real tests.
The must-haves in this agentic AI engineer job description:
Full-time pay is $125,000 per annum in USD. At an assumed ₹95 per dollar that is close to ₹1.19 crore, a rough conversion that changes whenever the rupee does. Work is largely async, through design notes, pull requests and test results. Expect some overlap hours for reviews and incidents, agreed during hiring.
Every agent starts as a goal in plain words. The brief for this illustrative build: "Help a user research their mutual fund holdings, remember their portfolio over months, and never quote a holding the user has since changed." That last clause is the engineering task. Each memory is a Postgres row with an embedding, a user ID, a subject key such as "holding:fund_x" and a valid-to date. Retrieval pulls the five closest memories per turn. Two numbers decide whether it works.
Failures clustered. Asked "how many units do I hold?", the agent kept retrieving the March memory, because "I hold 200 units" sits closer to the question than "I sold half of it". Similarity search worked; the design was wrong.
The agent reaches its tools through a small MCP server with three tools: fetch a fund's latest NAV, search stored factsheets and write a memory. The fix went into the write tool. Before saving, it finds any active memory with the same subject key and closes it with a valid-to date. Retrieval skips closed memories and adds a light recency weight. One reviewer comment on the pull request: "What happens when two memories disagree and neither has a subject key?" That became a new test, where the agent must ask the user.
Recall rose to 37 of 40 and the hit rate to about 91%. The suite's rubric line: pass only if the answer uses the newest valid memory and names its date. At home, a laptop with 16 GB of RAM runs Docker, a local Postgres with pgvector and the MCP Inspector.
The agentic AI engineer skills that matter most are the ones above: Python, memory design, MCP servers, vector retrieval and evaluation. Six months is realistic only if you already program. Spend two months on Python testing and Postgres, two on a retrieval project with honest hit-rate numbers, and two on an MCP server and an agent with memory.
An agentic AI engineer course helps when it ends in something you built. The IBM RAG and Agentic AI Professional Certificate on Coursera covers retrieval and agent building, and it pays off if you extend one of its projects. No agentic AI engineer certification replaces a public repository with tests.
With three years required, this post is not one of the entry level remote agentic AI engineer jobs. Searches for agentic AI engineer jobs no experience mostly surface internships or junior backend roles, which are a sensible first step.
A strong agentic AI engineer resume puts numbers on agent behaviour: "Raised memory recall from 72% to 92% on a 40-conversation suite by adding superseded-memory handling." Link a repository a reviewer can run in ten minutes. Expect questions like:
A generative AI engineer mostly builds features where a model produces content in one pass, such as a summary or a grounded answer. Agentic work adds a loop in which the model plans, calls tools, keeps state and acts, so failures differ: repeated tool calls, runaway token bills, actions taken on old facts.
Demand has grown since MCP was released as an open standard in late 2024, and remote boards such as Himalayas list agent-focused roles regularly. Titles still shift, so judge each offer on its actual work.
Be suspicious of any "onboarding kit" or certification fee, any request to share a banking OTP or UPI PIN, recruiters who stay on Telegram or WhatsApp, and offers made without a technical interview or take-home. No payment is ever asked for here, and applications cost nothing.
Among agentic AI engineer jobs remote worldwide, this one is open anywhere. Send your resume, one repository link and a short note on a memory bug you fixed. If you reached this page searching for agentic AI engineer work from home jobs or remote agentic AI engineer jobs hiring now, the note is what the hiring team reads first. Selection rests on the interview and take-home, with no outcome guaranteed.