Job description
Key Responsibilities
- 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.
Required Skills & Qualifications
- 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
