We are looking for a Senior Developer with strong foundations in Data Science, Machine Learning, and Generative AI (LLMs).

You will be responsible for designing, building, and deploying intelligent data?driven systems from traditional ML pipelines to LLM?based applications integrated into production environments. This is a hands?on role that blends data science, software development, and cloud-based deployment. You will work closely with product, data, and engineering teams to turn business ideas into scalable AI solutions.

Job Description :

- Design and implement end-to-end ML pipelines including data preprocessing, feature engineering, and model training.

- Build and fine?tune classical ML models and basic deep learning models for structured and unstructured data.

- Develop and deploy LLM-based applications using frameworks like LangChain, LangGraph, and Databricks Agent Bricks.

- Integrate agentic LLM systems for automation, reasoning, and decision?making workflows.

- Implement NLP models for text understanding, summarization, classification, and information extraction using BERT, Transformers, or NLTK.

- Work with different embedding techniques (word, sentence, document embeddings) to build semantic search and retrieval systems.

- Deploy and manage models on cloud environments (AWS, Azure, or Databricks).

- Use MLOps tools (MLflow, Databricks Model Registry, CI/CD) for model versioning, deployment, and monitoring.

- Design interactive pipelines and APIs for ML/LLM services.

- Collaborate with data engineers and software developers to ensure production?grade performance, reliability, and scalability.

Must-Have :

- Strong programming experience in Python (including Pandas, Scikit?learn, PyTorch/TensorFlow basics).

- Solid understanding of Data Science concepts data cleaning, feature engineering, model evaluation, hyperparameter tuning.

- Hands?on experience with LLMs and agentic frameworks (LangChain, Databricks Agent Bricks, LangGraph, OpenAI API, or similar).

- Familiarity with NLP libraries NLTK, HuggingFace Transformers, SpaCy.

- Experience in deploying models using MLOps practices (MLflow, Docker, CI/CD, Model Registry).

- Knowledge of cloud platforms (Databricks, AWS, Azure) and data workflows (Delta Lake, Unity Catalog).

- Understanding of RESTful APIs and interactive dashboards for AI/ML solutions.

- Good problem's olving skills and ability to translate business needs into data?driven solutions.

Nice to Have :

- Exposure to retrieval-augmented generation (RAG) and vector databases.

- Knowledge of data orchestration tools (Airflow, Kedro, or Prefect).

- Understanding of LLMOps and evaluation metrics for LLM performance.

- Experience with multi-agent systems and agent-based orchestration in Databricks.