Overview:
We are seeking AI/ML professionals to design, develop, and deploy intelligent systems that solve real-world problems at scale. The role involves working across the full lifecycle of AI solutions — from data collection and model development to deployment and monitoring — while collaborating with cross-functional teams to align with business goals.
Responsibilities:
- Collect, clean, and preprocess structured and unstructured datasets.
- Design, train, and evaluate machine learning and deep learning models (NLP, CV, recommendation, predictive analytics).
- Build and optimize scalable data pipelines for training and inference.
- Deploy AI/ML models into production environments (cloud, on-prem, edge).
- Monitor and maintain deployed models for performance, drift, and bias.
- Collaborate with product, engineering, and research teams to integrate AI solutions into applications.
- Conduct experiments, A/B testing, and performance benchmarking.
- Document workflows and communicate insights to stakeholders.
Required Skills/Tools:
- Programming & Frameworks: Python, R, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy.
- ML Lifecycle & Ops: MLflow, Kubeflow, Airflow, Docker, Kubernetes, CI/CD pipelines, monitoring tools.
- Data & Analytics: SQL, Spark, Hadoop, Jupyter, Tableau/Power BI, distributed computing frameworks.
- Specialized Areas: Hugging Face, SpaCy, NLTK, OpenCV, YOLO, Detectron2, LangChain, vector databases (FAISS, Pinecone).
- Cloud & Infrastructure: AWS SageMaker, GCP Vertex AI, Azure ML, Terraform, GPU/TPU acceleration (CUDA).
- Research & Advanced ML: Transformers, GANs, VAEs, GNNs, reinforcement learning, LaTeX for documentation.
Key Competencies:
- Strong understanding of machine learning, deep learning, and data science fundamentals.
- Ability to translate business problems into AI/ML use cases.
- Experience with both prototyping and deploying production-grade systems.
- Problem-solving mindset with attention to scalability, accuracy, and efficiency.
- Collaboration across data science, engineering, research, and product teams.
- Continuous learning and curiosity about new AI/ML advancements.