AI/ML Engineer - Production AI & LLM Systems
New
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JobgetherArtificial intelligence
Ability to work remotely from India, Mandatory overnight schedule aligned with US business hours: Monday through Friday, either 7:30 PM–4:30 AM IST or 8:30 PM–5:30 AM IST, depending on US daylight saving time.Full-TimeMiddle
Salary1,500,000 - 2,200,000 INR per year
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Job Details
- Experience
- 3+ years of hands-on AI/ML engineering experience
- Required Skills
- DockerKerasMachine LearningPyTorchFastAPINLPLLM
Requirements
- Have 3+ years of hands-on AI/ML engineering experience, including experience deploying at least one AI/ML system into production.
- Understand NLP fundamentals, including tokenization, embeddings, classification, named entity recognition (NER), and information extraction.
- Have software engineering skills in coding, Git, testing, debugging, API development, and system design.
- Have practical experience training machine learning models using PyTorch or Keras.
- Understand generative model architectures, including Transformers and Variational Autoencoders (VAEs).
- Have production experience with LLM-based systems, including RAG, embeddings, vector databases, prompt engineering, and evaluation methodologies.
- Have experience developing APIs using FastAPI or similar frameworks.
- Be familiar with Docker and at least one major cloud platform: AWS, GCP, or Azure.
- Be able to read machine learning research papers, evaluate technical approaches, and translate findings into working code.
- Be comfortable working the mandatory overnight schedule aligned with US business hours, Monday through Friday.
- Be able to work remotely from India and collaborate with international teams across time zones.
- Be flexible to occasionally support weekend work or schedule adjustments when required.
Responsibilities
- Design, develop, and deploy production-ready AI systems, including LLMs, RAG pipelines, agentic workflows, and machine learning and deep learning solutions.
- Select models, architectures, and technologies while balancing accuracy, latency, cost, scalability, and complexity.
- Train, fine-tune, and optimize machine learning models, including data preparation and model evaluation.
- Research, implement, and benchmark emerging AI techniques before integrating them into production.
- Develop APIs, inference pipelines, backend integrations, and supporting infrastructure for AI-powered applications.
- Define and monitor metrics for model quality, latency, cost, and reliability.
- Evaluate, debug, and optimize deployed AI systems, identifying potential regressions before they affect users.
- Collaborate with software engineers, backend developers, product teams, and engineering leadership to integrate AI capabilities into applications and workflows.
- Document system architecture, implementation strategies, technical decisions, and operational processes.
- Review and reproduce machine learning research, translating relevant findings into practical engineering solutions.
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