Research Engineer | Title: AI Research Engineer

RiDiK; Bangalore Rural; On-site; INR 900,000 - 1,500,000; Full time

Job summary

RiDiK in Bangalore is seeking a Research Engineer to lead AI feature development and architect scalable ML systems for production products.

You will build LLM, CV, and multimodal pipelines, deploy models with containerized microservices (ECS/K8s), and focus on reliability and cost efficiency, incident response, and postmortems to reduce MTTR. Strong Python and ML framework experience required.

Qualifications

• Bachelor's or Master's in CS/AI from IIT/Tier-1 or equivalent.
• 1–5+ years in applied ML/AI roles.
• Expert in Python with PyTorch or TensorFlow.
• Experience with end-to-end ML pipelines (data prep, training, deployment, monitoring).
• Proven track record shipping ML-powered products to real users.
• Hands-on with MLOps tooling (MLflow, Weights & Biases, DVC, Airflow, Prefect).
• Knowledge of containerized deployments (Docker, ECS, K8s) and CI/CD for ML.
• Strong fundamentals in statistics, experimentation, and interpreting real-world feedback.
• Experience optimizing GPU inference (ONNX, TensorRT, mixed precision, batching).

Responsibilities

• Lead design and development of core AI features - from data ingestion to real-time inference for production products.
• Architect ML systems and services that are scalable, cost-efficient, and observable.
• Build LLM, CV, or multimodal (ML) training pipelines (fine-tuning, adapters, retrieval, and evaluation) depending on product needs.
• Define model evaluation frameworks (offline metrics + live A/B + user feedback loops).
• Collaborate with Software, Data, and Product teams to design features powered by ML.
• Deploy and monitor models using containerized microservices (ECS/K8s), ensure low-latency inference and reproducibility.
• Own incident response and postmortems for AI systems, improve reliability and reduce MTTR.
• Optimize training/inference cost (batching, quantization, mixed precision, GPU scheduling).

Skills

Python programming

ML fundamentals

ML system design

Education

Bachelors/Masters in CS/AI (IIT/Tier-1)

Tools

PyTorch

TensorFlow

MLflow

Job description

The Research Engineer will be involved in AI feature development and architect scalable ML systems. The position focuses on building LLM, CV, and multimodal ML pipelines, deploying models in production, and ensuring reliability and cost efficiency.

Key Responsibilities

• Lead design and development of core AI features - from data ingestion to real-time inference for production products.
• Architect ML systems and services that are scalable, cost-efficient, and observable.
• Build LLM, CV, or multimodal (ML) training pipelines (fine-tuning, adapters, retrieval, and evaluation) depending on product needs.
• Define model evaluation frameworks (offline metrics + live A/B + user feedback loops).
• Collaborate with Software, Data, and Product teams to design features powered by ML.
• Deploy and monitor models using containerized microservices (ECS/K8s), ensure low-latency inference and reproducibility.
• Own incident response and postmortems for AI systems, improve reliability and reduce MTTR.
• Optimize training/inference cost (batching, quantization, mixed precision, GPU scheduling).

Required Qualifications

• Education: Bachelors/ Masters from a top-tier institute (IIT/Tier-1 etc.) in Computer Science, AI, or related field.
• 1–5+ years in applied ML/AI roles
• Expert in Python, strong in at least one deep-learning framework (PyTorch/TensorFlow).
• Experience with end-to-end ML pipelines (data prep, training, evaluation, deployment, monitoring).
• Proven success shipping ML-powered products not just models to real users.
• Hands-on with MLOps tooling (MLflow, Weights & Biases, DVC, Airflow, Prefect, etc.).
• Knowledge of containerized deployments (Docker, ECS, K8s) and CI/CD for ML.
• Strong fundamentals in statistics, experimentation, and interpreting real-world feedback.
• Experience optimizing/operating GPU inference (ONNX, TensorRT, mixed precision, batchin