AI/ML Engineer - Production AI & LLM Systems

India - Fully Remote, Monday through Friday, 7:30 PM–4:30 AM or 8:30 PM–5:30 AM IST in India, depending on U.S. daylight saving time; occasional weekend availability or schedule adjustments may be required.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, with at least one system deployed to production.
Required Skills: AWSDockerGCPKerasMicrosoft AzurePyTorchFastAPINLP

Requirements

Have 3+ years of hands-on AI/ML engineering experience.
Have deployed at least one system to production.
Know NLP fundamentals, including tokenization, embeddings, classification, NER, and information extraction.
Have engineering skills across coding, Git, testing, API/system design, and debugging.
Have hands-on model-training experience using PyTorch or Keras.
Understand generative model architectures, including Transformers and VAEs.
Have production LLM experience, including RAG, embeddings/vector databases, and prompt/evaluation design.
Have experience with FastAPI or similar frameworks, Docker, and at least one cloud platform such as AWS, GCP, or Azure.
Be able to read ML research papers and translate ideas into working code.
Work Monday through Friday on the assigned overnight IST schedule; occasional weekend availability or schedule adjustments may be required.
Preferred: experience with LLM fine-tuning, including LoRA and RL-based methods.
Preferred: SQL database knowledge, familiarity with LangGraph or LangChain, and experience with Kubernetes, CI/CD, or model-serving infrastructure.
Preferred: open-source contributions or experience reproducing research results.

Responsibilities

Design, develop, and deploy AI systems, including LLMs, RAG, agentic workflows, and ML/DL solutions.
Select models and architectures while balancing accuracy, latency, cost, and complexity.
Train and fine-tune models using PyTorch or Keras, including data preparation and evaluation.
Implement and benchmark research methods before adopting them.
Build APIs, inference pipelines, and backend integrations.
Evaluate metrics and track system quality, latency, and cost.
Monitor, debug, and optimize AI systems after launch.
Ensure production readiness and identify regressions before they affect users.
Collaborate with software engineers and product teams to integrate AI into applications.
Document architecture, implementation approaches, and technical decisions.

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