AI/ML Engineer

Creuto Cloud Private Limited; India; On-site; INR 800,000 - 1,600,000; Full time

Job summary

Creuto Cloud Private Limited seeks an AI/ML Engineer to design and deploy machine learning models, ensuring high-quality AI features in production. You will collaborate with product managers and data teams, focusing on user impact while optimizing AI outputs.

The ideal candidate has 2 to 5 years of experience, is proficient in Python, and understands the intricacies of large language models and prompt engineering. This role offers a unique opportunity to shape AI capabilities within the company's products.

Qualifications

• 2 to 5 years of hands-on experience building and shipping ML models or AI features in production environments.
• Proficiency in Python and experience with relevant ML frameworks.
• Understanding of prompt engineering and ML lifecycle management.

Responsibilities

• Design, train, evaluate, and deploy machine learning models into production systems.
• Build data pipelines and integrate large language models into product workflows.
• Monitor AI features for quality and performance in production.

Skills

Python

ML frameworks (PyTorch, TensorFlow, JAX)

Large Language Models (LLMs)

Model evaluation and monitoring

Data preprocessing and feature engineering

Tools

MLflow

Weights and Biases

Kubeflow

Vector databases (Pinecone, Weaviate)

Job description

About the Role

We are looking for an AI/ML Engineer with 2 to 5 years of experience to build, train, evaluate, and deploy machine learning models and AI-powered features directly into our products. You will work closely with product managers, backend engineers, and data teams to identify where AI creates the most user value, then build and ship those capabilities at production quality. This is a role for engineers who are as comfortable writing a training loop as they are debugging a production inference endpoint.

In This Role, You Will

• Design, train, evaluate, and deploy machine learning models and LLM‑powered features into production systems.
• Build pipelines for data collection, preprocessing, feature engineering, and model training using Python and relevant ML frameworks.
• Integrate large language models from providers such as OpenAI, Anthropic, and Google into product workflows using prompt engineering and fine‑tuning.
• Build retrieval augmented generation (RAG) systems using vector databases such as Pinecone, Weaviate, or pgvector.
• Develop evaluation frameworks and benchmarks to measure model quality, accuracy, and performance over time.
• Optimise models for latency and cost in production including quantisation, distillation, and batching strategies.
• Monitor AI features in production for quality degradation, hallucination rates, and unexpected behaviours.
• Collaborate with product and engineering to translate user problems into well‑defined ML tasks and deliverables.
• Stay current on the latest AI research, models, and tooling and bring relevant advances into the team work.
• Document models, data pipelines, and evaluation results clearly for both technical and non‑technical stakeholders.

You Might Thrive in This Role If You

• Have 2 to 5 years of hands‑on experience building and shipping ML models or AI features in production environments.
• Are proficient in Python and experienced with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Have worked with LLMs in production and understand the nuances of prompt engineering, context management, and output reliability.
• Are comfortable with the full ML lifecycle from data preparation and training through to deployment and monitoring.
• Can evaluate model quality rigorously and are not satisfied with models that work well on benchmarks but fail in real use.
• Think about user impact first and use technical sophistication in service of solving real problems.
• Communicate clearly about model limitations, tradeoffs, and uncertainties to non‑technical collaborators.

Bonus If You Have

• Experience fine‑tuning open source models such as LLaMA, Mistral, or Phi using LoRA or similar techniques.
• Familiarity with MLOps tooling including MLflow, Weights and Biases, or Kubeflow.
• Knowledge of multi‑modal AI including vision‑language models and image generation.
• Experience building agentic AI systems with tool use, memory, and multi‑step reasoning.
• Contributions to AI research, open‑source ML projects, or published papers.
• Understanding of responsible AI principles including bias detection, fairness, and model safety.