Senior Engineer – AI (Artificial Intelligence)

About the Role:

We're seeking a skilled and motivated ML/AI Engineer to join our team and drive end-to-end AI development for cutting-edge healthcare prediction models. As part of our AI delivery team you will be working on designing, developing, and deploying ML models to solve complex business challenges. 

Key Responsibilities: 

• Design, develop, and deploy scalable machine learning models and AI solutions. 
• Collaborate with engineers and product managers to understand business requirements and translate them into technical solutions. 
• Analyse and preprocess large datasets for training and testing ML models. 
• Experiment with different ML algorithms and techniques to improve model performance. 

Experience: 

• B.Tech or M.Tech or M. Statistics with 3-6 years of relevant experience in Machine Learning, AI, or Data Science roles. 
• Education: Bachelor's/master's degree in computer science or Statistics or any other relevant engineering / AI course from a Tier 1 or Tier 2 college. 

Technical Skills: 

• Strong Python programming and software engineering skills.
• Hands-on experience with classification, regression, clustering, and ensemble models such as Random Forest and XGBoost.
• Strong understanding of Transformer architecture, including attention mechanisms, embeddings, tokenization, and encoder/decoder models.
• Experience working with LLMs and SLMs, including model selection, supervised fine-tuning, and LoRA/QLoRA.
• Proficiency in PyTorch, Hugging Face Transformers, PEFT, and Scikit-learn.
• Experience preparing training datasets, evaluating models, and assessing accuracy, hallucinations, latency, and cost.
• Working knowledge of NLP, embeddings, vector databases, and RAG.
• Experience with GPU workloads and deploying model APIs using FastAPI/Flask, Docker, and cloud platforms—Azure preferred.
• Familiarity with MLOps, model monitoring, and explainability techniques such as SHAP or LIME.

Nice to Have: 

• Model optimization through quantization, knowledge distillation, and Transformer layer pruning.
• Experience with agentic AI, distributed training, or inference optimization.