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Jr. Software Engineer

ForageAI
|Remote|4 Jun 2026
Experience Required
2-3 Years
Employment Type
Full-time
Target Batch
Any
Role Category
Fullstack
How to Apply
Click on the Apply button
Skills Recommended
PythonSQLNoSQLvector databasesweb scraping frameworkssystem designdistributed systemsAWSLinuxGitGenAILLMsLangChainCrewAI
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About the job

Job Description

About the job

Location: Remote (Work from Home)

About Forage AI

ForageAI builds next‑generation systems for data collection and processing — large‑scale web crawling, document parsing, data pipelines, and automation. We work primarily in Python, leverage cloud‑native designs (mainly AWS, with exposure to GCP/Azure), and increasingly apply GenAI and AI agents across our stack. Every developer owns their module and collaborates closely with peers in a high‑ownership, high‑trust environment

Role Overview

As a Jr. Software Engineer, you will work on software systems for data collection, processing, enrichment, and automation at scale. This is a hands-on engineering role where you will write production-quality code, debug real-world data problems, work with data pipelines, and gradually take ownership of modules.

You will also get opportunities to work with GenAI-based systems, LLM workflows, AI agents, and modern coding assistants. We encourage the use of coding co-pilots and AI tools, but with strong engineering discipline. You should be able to understand, review, test, and take full responsibility for the code you write or generate using these too

Key Responsibilities: ·

Develop and maintain Python applications for crawling, parsing, enrichment, and processing of large datasets.·

Build and operate data workflows/ Datapipelines, ETL/ELT, including validation, monitoring, and error‑handling.

Work with SQL and NoSQL (plus vector databases/data lakes) for modeling, storage, and retrie

val.· Contribute to system design using cloud‑native components on AWS (e.g., S3, Lambda, ECS/EKS, SQS/SNS, RDS/DynamoDB, CloudWatch).

Build LLM-based systems, RAG workflows, AI agents, and GenAI-enabled automation modules. Use coding co-pilots and AI development tools responsibly to improve productivity, while ensuring the code is understood, tested, secure, and maintainable.

Implement and consume APIs/microservices; write clear contracts and documentation.

Write unit/integration tests, perform debugging and profiling; contribute to code reviews and maintain high code quality.

Implement observability (logging/metrics/tracing) and basic security practices (secrets, IAM, least privilege).

Collaborate with Dev/QA/Ops; ship incrementally using PRs and design docs.

Required Qualifications

2–3 years of professional software engineering experience.·

Strong proficiency in Python; good knowledge of data structures/algorithms and basic software design principles.

Hands‑on with SQL and at least one NoSQL store; familiarity with vector databases is a

plus.

Experience with web scraping frameworks (e.g., Scrapy, Selenium/Playwright, BeautifulSoup) and resilient crawling patterns (respect robots/rotations/retries).

Practical understanding of system design and distributed systems basics.

Exposure to AWS services and cloud‑native design; comfortable on Linux and with Git.

GenAI & LLMs: experience with LangChain, CrewAI, LlamaIndex, prompt design, RAG patterns, and vector stores. (Candidates with this experience will be prioritized.)

Preferred / Good to Have (Prioritized):

CI/CD & Containers: exposure to pipelines (GitHub Actions/Jenkins), Docker, and Kubernetes.

Data Pipelines/Big Data: ETL/ELT, Airflow, Spark, Kafka, or similar.

Infra as Code: Terraform/CloudFormation; basic cost‑ and performance‑optimization on cloud.

Frontend/JS: not required; basic JS or frontend skills are a nice‑to‑have only.

Exposure to GCP/Azure.

How We Work:

Ownership of modules end‑to‑end (design → build → deploy → operate).

Clear communication, collaborative problem‑solving, and documentation.

Pragmatic engineering: small PRs, incremental delivery, and measurable reliability.

Work‑from‑Home Requirements

High‑speed internet for calls and collaboration.

A capable, reliable computer (modern CPU, 16GB + RAM).

Headphones with clear audio quality.

Stable power and backup arrangements.

🎯 Why This Role Matters

As a Jr. Software Engineer at ForageAI, you will be at the forefront of solving complex problems that impact millions of users. This is not just about writing code or executing tasks; it is about taking ownership of critical systems, collaborating with top-tier talent, and driving innovation. If you want a role that challenges you to grow rapidly and leaves a lasting impact on the industry, this is it.

Key Skills Needed

To stand out for this position, you need more than just the basics. Hiring managers for this Jr. Software Engineer role are looking for:

  • Strong foundational knowledge in core engineering principles.
  • Ability to adapt quickly to the fast-paced environment at ForageAI.
  • Proficiency in Python, SQL, NoSQL.

💡 Application Tips

  • Tailor your resume: Highlight specific projects or experiences that align directly with current initiatives at ForageAI.
  • Prepare for behavioral rounds: Be ready to discuss times you have handled failure, tight deadlines, or team conflicts.
  • Leverage the AI Assistant: Use the AI Assistant button above to evaluate your resume against this specific Jr. Software Engineer description before applying.
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Candidate Guide

More than a copied JD: use this page to prepare before you apply.

ForageAI is hiring for Jr. Software Engineer in Remote. This page goes beyond the raw listing so students can understand what the role usually expects, how to prepare for screening, and how to apply more thoughtfully instead of forwarding a resume blindly.

PythonSQLNoSQLvector databasesweb scraping frameworkssystem designdistributed systemsAWS

Company overview

ForageAI appears on Campus to Career because the opportunity is relevant for students and early-career candidates who want a clearer view of real hiring demand. When evaluating any employer, students should look beyond the brand name and focus on work quality, reporting structure, product maturity, mentorship, and the kind of ownership the team is likely to trust a new hire with.

A fresher or internship role at ForageAI can be valuable when the candidate understands what the business is solving and how the team contributes to that larger outcome. Even before the interview, students should try to learn the company domain, customer type, pace of execution, and whether the role sits close to product, platform, support, data, or delivery.

What this role usually means in practice

Jr. Software Engineer is likely not just a keyword match. In real hiring, titles often compress multiple expectations into one label. This means the student should read the listing as a signal of day-to-day problem solving, team collaboration, deadline discipline, and the ability to learn new workflows quickly.

The current role is listed as Full-time in Remote, with 2-3 Years mentioned on the page. For freshers, the most useful interpretation is: what kind of output will the team expect in the first 30 to 90 days, and what proof can the candidate show that they are ready to deliver it?

  • Understand the business problem the role supports
  • Map your projects to likely day-to-day work
  • Prepare one story about fast learning and one about ownership

Required skills and how to interpret them

The listing highlights skills such as Python, SQL, NoSQL, vector databases, web scraping frameworks, system design, distributed systems, AWS. Students should not panic if they are not equally strong in every item. Companies often list an ideal stack, but interviewers usually look for transferable understanding, clarity of fundamentals, and a believable proof-of-work story.

A better preparation strategy is to sort skills into three buckets: already strong, interview-ready but shallow, and currently weak. This prevents overconfidence and also stops students from wasting time revising topics that are unlikely to matter during the first screening round.

  • Be ready to explain where you used Python in a project, coursework, internship, or self-study build.
  • Be ready to explain where you used SQL in a project, coursework, internship, or self-study build.
  • Be ready to explain where you used NoSQL in a project, coursework, internship, or self-study build.
  • Be ready to explain where you used vector databases in a project, coursework, internship, or self-study build.
  • Be ready to explain where you used web scraping frameworks in a project, coursework, internship, or self-study build.
  • Be ready to explain where you used system design in a project, coursework, internship, or self-study build.
  • Be ready to explain where you used distributed systems in a project, coursework, internship, or self-study build.
  • Be ready to explain where you used AWS in a project, coursework, internship, or self-study build.

Eligibility and application readiness

Students should treat eligibility as more than just degree, batch, or marks. Real readiness also includes whether the resume supports the role clearly, whether your GitHub or portfolio can survive a quick recruiter scan, and whether your self-introduction makes logical sense for Jr. Software Engineer.

If the listing mentions a batch requirement, relocation, internship-to-full-time path, or communication expectations, make sure those details are reflected consistently in your resume, application form, and outreach message. Consistency is a major trust signal in early-stage screening.

  • Resume aligned to the role and keywords
  • Portfolio or GitHub links working correctly
  • Projects chosen based on role relevance, not just recency
  • Clear answer prepared for “Why this role?”

Salary insight and offer evaluation

The listing does not clearly publish compensation, which is common for fresher and early-career openings. Candidates should use peer benchmarks, city cost, and recruiter conversations to understand likely salary range before final acceptance.

For freshers, salary should be interpreted together with learning quality, tech exposure, mentorship, workload, location, and conversion or growth path. A slightly smaller offer with stronger ownership and cleaner learning loops may outperform a bigger offer that provides weak role fit or no meaningful skill depth.

Interview preparation tips for this job

Candidates applying for Jr. Software Engineer should prepare in layers. The first layer is role fit: why this company, why this role, and what proof supports your application. The second layer is technical or functional depth: the tools, concepts, or workflows most likely to appear in screening. The third layer is behavior and communication: clear explanations, honest ownership, and calm thinking when details are incomplete.

A strong practice method is to prepare a short project walk-through, a role-fit introduction, one debugging or challenge story, and a realistic answer to what you still want to learn. That combination usually performs better than memorizing long theoretical scripts.

  • Review two strongest projects deeply, not ten projects weakly
  • Prepare role-specific terminology and examples
  • Practice concise answers for HR and recruiter rounds
  • Revise fundamentals likely connected to the listed skills

Application strategy for better conversion

The best candidates do not just click apply. They adapt. Before submitting, update the top section of your resume, reorder projects if needed, and make sure your strongest evidence matches the narrative for Jr. Software Engineer. If the company uses an external portal, take form fields seriously because ATS filters often read those signals separately from the PDF.

If the route is recruiter email or a direct apply link, use that path professionally. Submit complete information, avoid spammy follow-up, and if you choose to reach out on LinkedIn, mention the role, one or two fit points, and a respectful ask. The goal is to make your application easier to trust, not louder.