Sr. Machine Learning Engineer

Company Description

IndiaNIC Infotech Limited, established in 1998 in Ahmedabad, is a globally recognized leader in AI development, machine learning solutions, and software services. With a team of 600+ experts across multiple offices in India, the USA, Australia, and the Emirates, IndiaNIC has delivered over 7,000 AI-powered projects to a diverse client base of more than 3,000 clients worldwide. As the recipient of the “Best AI Innovation Company 2021” award by the Economic Times, IndiaNIC continues to excel in providing custom AI and machine learning solutions, generative AI services, and rapid prototype development. Known for its innovative approaches and commitment to excellence, the company fosters strong, long-lasting partnerships and offers exceptional career growth opportunities.

Role Description

This is a full-time, on-site role for a Senior Machine Learning Engineer located in Ahmedabad. The Senior Machine Learning Engineer will design, develop, and deploy cutting-edge machine learning models and solutions. Responsibilities include implementing advanced algorithms, optimizing neural networks, analyzing large-scale data to extract insights, collaborating with cross-functional teams, and ensuring the scalability and robustness of AI systems. The engineer will also contribute to innovation by staying updated on the latest AI advancements and applying them to solve complex problems.

Qualifications

 - 5+ years hands-on experience as an ML Engineer building and deploying production AI systems

 - Strong proficiency in PyTorch and/or TensorFlow — model training, fine-tuning, knowledge distillation, and quantization                                                              

 - Experience with computer vision models: YOLO, SAM 2, DINOv2, SegFormer, or similar segmentation/detection architectures                                                              

 - Experience with NLP/LLMs: fine-tuning transformers (BERT, XLM-RoBERTa, LLaVA-Next), prompt engineering, RAG pipelines (LangChain, LlamaIndex, Qdrant)                                               

 - Proficiency in structured ML: XGBoost, CatBoost, LightGBM — feature engineering, ensemble methods, calibration                                                                  

 - Hands-on with GPU training pipelines: NVIDIA A10G/A100/T4, CUDA, W&B experiment tracking, Jupyter, Google Colab                                                                  

 - Experience deploying models to production: FastAPI, Uvicorn, NVIDIA Triton, Docker, Kubernetes                                                                          

 - Familiarity with MLOps tooling: MLflow (model registry), Evidently AI (drift monitoring), Prefect (orchestration)                                                                 

 - Working knowledge of vector databases: Milvus, Qdrant, or Pinecone                                                                                        

 - Experience with cloud platforms: AWS or GCP — compute, storage, GPU instances                                                                                  

 - Strong Python skills. Familiarity with Redis, Supervisor, Nginx for production serving                                                                              

 - Bonus: experience with fraud detection, anomaly detection (Isolation Forest), or insurance/healthcare AI domains                                                                 

 - Bonus: experience with mobile model deployment (CoreML, TFLite, ONNX)