The ideal candidate should be capable of handling end-to-end developmentfrom building scalable backend services and interactive front-end applications to developing ML models and managing data workflows.

Key Responsibilities :

- Develop, maintain, and enhance scalable backend APIs and microservices using Python (FastAPI/Django/Flask).

- Build responsive and user-friendly frontend applications using React.js / Angular.

- Integrate front-end and back-end components to deliver seamless product experiences.

- Ensure application performance, security, scalability, and reliability.

- Develop, train, evaluate, and deploy machine learning and deep learning models.

- Implement data preprocessing, feature engineering, model validation, and optimization pipelines.

- Work with ML frameworks such as TensorFlow, PyTorch, Scikit-learn.

- Deploy ML models into production using APIs, containers, or cloud services.

- Design and implement ETL/ELT pipelines, integrating data from multiple structured and unstructured sources.

- Work with databases such as PostgreSQL, MySQL, MongoDB, Redis, etc.

- Manage data ingestion, transformation, storage, and retrieval.

- Optimize data workflows for performance and scalability.

- Deploy and manage applications on cloud platforms such as AWS, Azure, or GCP.

- Create CI/CD pipelines using tools like GitHub Actions, Jenkins, GitLab CI/CD, etc.

- Containerize applications using Docker; orchestrate using Kubernetes is a plus.

- Work closely with product managers, UI/UX designers, data scientists, and other stakeholders.

- Create clear documentation for code, APIs, data structures, and ML workflows.

- Participate in code reviews and contribute to continuous improvement practices.

Required Skills :

- Strong proficiency in Python and frameworks like Django, Flask, or FastAPI.

- Frontend development using React.js / Angular / Vue.js.

- Strong understanding of RESTful API development, microservices architecture.

- Solid experience in AI/ML algorithms, model building, validation, and deployment.

- Experience with data engineering conceptsETL pipelines, data modeling, data transformation.

- Proficiency in SQL and NoSQL databases.

- Hands-on experience with Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch.

- Knowledge of cloud services (AWS/Azure/GCP).

- Experience with Docker, version control (Git), and CI/CD practices.

- Knowledge of MLOps tools (MLflow, Kubeflow, Airflow).

- Experience with big data tools (Spark, Hadoop).

- Understanding of LLMs, prompt engineering, or vector databases (Pinecone, Weaviate, FAISS).

- Experience with serverless architecture (AWS Lambda/Azure Functions).

- Strong problem-solving and analytical thinking.

- Excellent communication and stakeholder management.

- Ability to work in an agile, fast-paced environment.

- Strong ownership and accountability for deliverables