Job Description
Key Responsibilities
- Design, develop, and deploy machine learning and AI models
- Build and maintain data pipelines for training and inference
- Work on supervised and unsupervised learning algorithms
- Implement NLP, Computer Vision, or Deep Learning solutions
- Optimize models for performance, scalability, and accuracy
- Collaborate with data engineers, analysts, and product teams
- Deploy models using cloud platforms (AWS/Azure/GCP)
- Monitor model performance and perform continuous improvements
- Conduct data preprocessing, feature engineering, and model evaluation
- Document models, processes, and experiments
Required Skills & Qualifications
Technical Skills
Strong proficiency in Python
Experience with Machine Learning libraries:
Scikit-learn
TensorFlow / PyTorch
- Knowledge of data manipulation tools (Pandas, NumPy)
- Experience with ML algorithms (regression, classification, clustering)
- Familiarity with Deep Learning frameworks
- Knowledge of NLP or Computer Vision techniques
- Experience in model deployment (APIs, microservices)
- Strong understanding of statistics and probability
Additional Responsibilities:
Preferred Skills (Nice-to-Have)
- Experience with Generative AI / LLMs (GPT, LangChain, Hugging Face)
- Knowledge of MLOps tools (MLflow, Kubeflow, Airflow)
- Experience with Docker and Kubernetes
- Familiarity with Big Data tools (Spark, PySpark)
- Experience with REST APIs and microservices architecture
- Knowledge of cloud AI services (AWS SageMaker, Azure ML, GCP AI)
- Understanding of vector databases and embeddings (FAISS, Pinecone)
- Exposure to CI/CD pipelines and DevOps practices
