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Applications open Full-Time Job

AI Engineer (Remote)

Joblet-AI

  • India (Remote / Multiple), India
  • Competitive Package
  • 3-5 Years
  • Posted Today
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About the role

  • Role Title: AI/ML Engineer, Internal Platforms
  • Role Type: Full-time
  • Location: Remote

The Role

Join our team to build and improve a next-generation AI recruiting agent. You'll develop new capabilities, experiment with the latest LLMs and agent frameworks, and ship production-ready AI features that make the agent smarter, more reliable, and more useful.

What You'll Do

  • Build and improve AI agent capabilities using modern LLMs.
  • Develop backend services and APIs in Python.
  • Design and iterate on prompts, workflows, RAG, and agent orchestration.
  • Evaluate model performance and continuously improve quality through experimentation.
  • Collaborate with product and engineering teams to deliver AI-powered features.
  • Deploy and maintain scalable AI systems in production.
  • Stay current with the latest AI models, tools, and best practices.

What We're Looking For

  • Strong Python software engineering skills.
  • Experience building applications with LLMs and AI APIs.
  • Familiarity with agent frameworks such as LangChain, LangGraph, or LlamaIndex.
  • Experience building APIs with FastAPI or similar frameworks.
  • Experience with cloud platforms (AWS, GCP, or Azure).
  • Strong problem-solving skills and an experimental mindset.
  • Excellent communication and collaboration skills.

Preferred Qualifications

  • Experience shipping AI products or AI agents to production.
  • Familiarity with RAG, tool calling, model evaluation, or prompt engineering.
  • Contributions to open-source AI projects or personal AI applications.
  • Experience working in fast-paced, AI-first environments.

Compensation & Benefits Notice

The national pay range for this full-time position is base salary of $130,000 –$160,000 USD. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. micro1 provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high-performing, remote-first workforce.

micro1 is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to support@micro1.ai.

Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.

Disclaimer

The information contained in this job posting, including but not limited to role responsibilities, qualifications, compensation, and benefits, is provided for informational purposes only and does not constitute a binding offer of employment. micro1 reserves the right to amend, modify, or withdraw any portion of this posting at its sole discretion and without prior notice. All employment decisions are made in accordance with applicable laws and regulations.

What You’ll Do

  • Build and improve AI agent capabilities using modern LLMs.
  • Develop backend services and APIs in Python.
  • Design and iterate on prompts, workflows, RAG, and agent orchestration.
  • Evaluate model performance and continuously improve quality through experimentation.
  • Collaborate with product and engineering teams to deliver AI-powered features.
  • Deploy and maintain scalable AI systems in production.

Apply Now!

Eligibility & process

Who can apply

Seniority: Mid-Senior level. Employment type: Full-time

Skills & stack

Python Next.js FastAPI AWS Azure GCP Machine Learning Generative AI / LLMs

Selection process

Details are shared by the recruiter after shortlisting.

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Python, Next.js, FastAPI, AWS, Azure, GCP, Machine Learning, Generative AI / LLMs) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Joblet-AI Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Joblet-AI as a AI Engineer (Remote)?
Highlight Joblet-AI's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Joblet-AI Competitive Package
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