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:
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