AI Engineer, Data Science – Research

Job Description: • Design and implement ML/AI models for regression, classification, NLP, time series, and clustering with large datasets. • Develop and deploy Generative AI and LLM-based solutions (OpenAI, Azure OpenAI, Hugging Face, LangChain, RAG). • Design and develop scalable Agents by keeping performance, cost and reusability aspects. • Perform data purification, feature engineering and correlation analysis (Pearson, Spearman, Chi-Square). • Deal with extremely large or complex dataset ingestion in system via mongodb, clickhouse, datalake etc. • Define quick correlations on structured and unstructured databases. • Work with SQL/NoSQL/Vector databases for AI data management. • Apply MLOps practices for model versioning, automation, and monitoring. • Collaborate with engineers and product teams to integrate AI into business applications. • Stay current with emerging AI technologies and ensure continuous optimization of deployed models. • Familiarity with external tools like cursor, mermaid, lucid, figma, bolt, windsurf, draw.io etc. • Create RESTful APIs and AI microservices using FastAPI/Flask/Django, deploy on Docker/Kubernetes. Requirements: • Education: Bachelor’s or Master’s in Computer Science, Data Science, or AI-related field. • Experience: 5–10 years in Data Science or AI Engineering. • Technical Skills: Proficiency in Python, NumPy, pandas, scikit-learn, TensorFlow, PyTorch, Hugging Face. • Strong knowledge on ChatGPT, Anthropic, Gemini & opensource deployment with ollama/lmstudio. • Strong grasp of data cleansing, feature correlation, and statistical modelling. • Experience building APIs and microservices with FastAPI/Flask/Django. • Familiarity with Generative AI, RAG, embeddings, and prompt engineering. • Knowledge of MLOps, CI/CD, Docker, Kubernetes, and cloud (AWS/Azure/GCP). • Databases: PostgreSQL, MongoDB, DynamoDB, Pinecone, Chroma, Faiss. • Knowledge on VLMs, OpenCV, CNN, finetuning of LLMs, evaluation criteria for LLMs. Benefits: