DL
Research Engineer
Diverse Lynx India
🇮🇳 India
On-site
2 months ago
- AI
- Microservices
- ECS
- Kubernetes
- Incident Response
- Python
- PyTorch
- TensorFlow
- MLOps
- MLflow
- Weights & Biases
- Airflow
- Prefect
- Docker
- CI/CD
- ONNX
- TensorRT
- RAG
- LoRA
- Prometheus
- Grafana
- OpenTelemetry
2 months ago
Research Engineer | Title: AI Research Engineer
The Research Engineer will be involved inAI feature development and architect scalable ML systems. The position focuses on buildingLLM, 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.
- ArchitectML systems and services that are scalable, cost-efficient, and observable.
- BuildLLM, CV, or multimodal (ML) training pipelines (fine-tuning, adapters, retrieval, and evaluation) depending on product needs.
- Definemodel evaluation frameworks (offline metrics + live A/B + user feedback loops).
- Collaborate withSoftware, Data, and Product teams to design features powered by ML.
- Deploy and monitor models usingcontainerized microservices (ECS/K8s), ensure low-latency inference and reproducibility.
- Ownincident response and postmortems for AI systems, improve reliability and reduce MTTR.
- Optimize training/inference cost (batching, quantization, mixed precision, GPU scheduling).
- Education:Bachelors/Master's from atop-tier institute (IIT/Tier-1 etc.) in Computer Science, AI, or related field.
- 1–5+ years in applied ML/AI roles
- Expert inPython, strong in at least one deep-learning framework (PyTorch/TensorFlow).
- Experience withend-to-end ML pipelines (data prep, training, evaluation, deployment, monitoring).
- Proven success shipping ML-powered products not just models to real users.
- Hands-on withMLOps tooling (MLflow, Weights & Biases, DVC, Airflow, Prefect, etc.).
- Knowledge ofcontainerized deployments (Docker, ECS, K8s) and CI/CD for ML.
- Strong fundamentals instatistics, experimentation, and interpreting real-world feedback.
- Experience optimizing/operatingGPU inference (ONNX, TensorRT, mixed precision, batching).
- Familiar withvector databases,RAG pipelines, andLLM fine-tuning/adapters (LoRA/QLoRA).
- Exposure toobservability stacks (Prometheus/Grafana/OpenTelemetry) and production logging/metrics.
Research Engineer · Diverse Lynx India