AI Research Engineer

Epergne Solutions; Mumbai; On-site; INR 3,200,000 - 5,200,000; Full time

Job summary

Epergne Solutions in Bengaluru, India, seeks an AI Research Engineer to design, develop, and deploy scalable ML systems, including LLM, computer vision, and multimodal pipelines. You will build end-to-end training and inference workflows and deploy models into production.

The ideal candidate has 3–5+ years of experience, strong Python and ML framework skills, and hands-on experience with MLOps tools, Docker/Kubernetes, and GPU optimisation to improve reliability and cost efficiency.

Qualifications

• Experience in applied ML engineering and production systems.
• Strong stats, experimentation, and performance analysis.
• Experience deploying production ML applications.
• Understanding of model evaluation and real-world performance.

Responsibilities

• Design scalable ML features from data ingestion to real-time inference.
• Build cost-efficient, observable ML systems.
• Develop training and inference pipelines for LLMs and CV models.
• Create evaluation frameworks and online experiments.
• Collaborate with software, data, and product teams.
• Deploy, monitor, and maintain models in cloud/container environments.
• Investigate incidents and optimize reliability and costs.
• Optimise training/inference costs via batching, quantisation, mixed precision.

Skills

Python

PyTorch

TensorFlow

ML pipelines

MLOps tools

Docker

Kubernetes

CI/CD

GPU optimisation

RAG

LLM fine-tuning

Observability

Education

Bachelor’s or Master’s in CS/AI

Tools

MLflow

Weights & Biases

DVC

Airflow

Prefect

Job description

Job Description:

Job Role:

AI Research Engineer

Job Location:

Bengaluru, India

Experience:

3-5+ Years

Role Summary:

We are seeking an AI Research Engineer to design, develop, and deploy scalable machine learning systems and AI-powered features. The role focuses on building LLM, computer vision, and multimodal machine learning pipelines, deploying models into production, and improving system reliability, performance, and cost efficiency.

Key Responsibilities:

• Design and develop AI features from data ingestion through real-time model inference.
• Build scalable, cost-efficient, and observable machine learning systems and services.
• Develop training and inference pipelines for LLM, computer vision, and multimodal AI models.
• Create model evaluation frameworks, including offline evaluation, online experiments, and user feedback integration.
• Collaborate with software engineering, data, and product teams to deliver AI-powered features.
• Deploy, monitor, and maintain machine learning models using containerised and cloud-based infrastructure.
• Investigate production incidents, improve system reliability, and optimise operational performance.
• Optimise training and inference costs through batching, quantisation, mixed precision, and GPU resource management.

Required Skills:

• Strong proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.
• Experience building end-to-end machine learning pipelines, including data preparation, training, evaluation, deployment, and monitoring.
• Knowledge of MLOps tools such as MLflow, Weights & Biases, DVC, Airflow, or Prefect.
• Experience with Docker, Kubernetes, containerised deployments, and CI/CD practices.
• Understanding of GPU optimisation, ONNX, TensorRT, batching, and mixed precision techniques.
• Familiarity with vector databases, retrieval-augmented generation (RAG), and LLM fine-tuning approaches.
• Knowledge of observability and monitoring tools such as Prometheus, Grafana, or OpenTelemetry.
• Strong analytical, problem-solving, and collaboration skills.

Qualifications & Experience:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 3–5+ years of experience in applied machine learning, AI engineering, or software engineering.
• Experience developing and deploying production-grade machine learning applications.
• Understanding of statistics, experimentation, model evaluation, and real-world performance analysis.

Preferred Attributes:

• Hands-on experience with LLMs, computer vision, or multimodal AI systems.
• Ability to balance research innovation with production engineering requirements.
• Strong ownership mindset and experience working in cross-functional teams.
• Excellent communication and technical documentation skills