Position: Associate Data Scientist(ML Engineer)
We are seeking a talented Data Scientist/ML Engineer to join our growing team. In this
role, you will play a vital part in building and deploying machine learning and AI-driven
systems, selecting the right algorithms, and implementing scalable solutions in
production environments.
Responsibilities & Duties
- Develop and implement machine learning algorithms and models (e.g., regression, classification, clustering, NLP, etc.)
- Design and build AI-powered solutions, including intelligent automation, recommendation systems, and predictive analytics models
- Work on Generative AI use cases such as LLM-based applications, chatbots, summarization, and content generation
- Fine-tune and deploy pre-trained foundation models (LLMs, transformers) using techniques like prompt engineering, RAG (Retrieval-Augmented Generation), and embeddings
- Analyze large datasets to extract insights and create predictive models that improve business performance
- Build and deploy end-to-end machine learning pipelines from data preprocessing to model deployment in production environments
- Collaborate with data engineers to design scalable data architectures and workflows
- Evaluate and fine-tune model performance using metrics, cross-validation, and hyperparameter optimization techniques
- Apply deep learning models for computer vision, NLP, and other advanced AI use cases
- Integrate AI/ML models with APIs and work closely with software developers to ensure smooth deployment
- Work with vector databases and semantic search systems for AI applications
- Ensure responsible AI practices, including model explainability, bias mitigation, and data privacy
- Stay up-to-date with the latest research in AI/ML and incorporate cutting-edge techniques into solutions
- Communicate insights, results, and technical concepts to both technical and non-technical stakeholders
- Contribute to the continuous improvement of data science and AI development processes and tools
Qualifications & Skills
- 2–4 years of experience manipulating datasets and building statistical/ML models, with a Bachelor's/Master's/PhD in Machine Learning, Data Science, Applied Statistics, Mathematics, Computer Science, or a related field
- Strong programming skills in Python, R, and SQL for data analysis and model building
- Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn
- Exposure to Generative AI tools and frameworks (e.g., OpenAI APIs, Hugging Face, LangChain, LlamaIndex)
- Understanding of LLMs, embeddings, vector databases, and prompt engineering
- Familiarity with Scala, Golang, or Java is an added advantage
- Strong knowledge of statistics, hypothesis testing, and data analysis techniques
- Experience with data visualization tools such as Tableau, Power BI, or Python libraries (Matplotlib, Seaborn)
- Experience with big data technologies (Hadoop, Spark)
- Experience deploying models in production environments (AWS, GCP, or Azure)
- Knowledge of MLOps practices, CI/CD pipelines, and model monitoring
- Familiarity with version control (Git) and containerization tools (Docker, Kubernetes)
- Basic knowledge of SQL and NoSQL databases
- Excellent verbal communication and stakeholder management skills
- Strong problem-solving ability and attention to detail
- Eagerness to learn and experiment with emerging AI technologies
