Data Science or Machine Learning Engineer

We are seeking a skilled GenAI, Agentic AI, and Data Science Engineer to design, develop, and deploy AI-driven solutions that leverage Large Language Models (LLMs), autonomous AI agents, and advanced analytics. The ideal candidate will have experience in Generative AI, machine learning, data science, and agent-based frameworks to build intelligent systems that automate business processes and deliver actionable insights.

5-8 Years of Experience

• Design and develop Generative AI applications using LLMs such as GPT, Claude, Gemini, or open-source models.
• Build and deploy Agentic AI solutions capable of autonomous reasoning, planning, tool usage, and workflow orchestration.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases and enterprise knowledge sources.
• Fine-tune, evaluate, and optimize foundation models for domain-specific use cases.
• Perform data analysis, feature engineering, model development, and predictive analytics.
• Design machine learning and deep learning models for business problems.
• Integrate AI agents with APIs, databases, cloud services, and enterprise applications.
• Monitor model performance, improve prompt engineering strategies, and ensure responsible AI practices.
• Collaborate with business stakeholders, product teams, and data engineers to deliver AI solutions.
• Document technical designs, experiments, and deployment processes.

Generative AI

• Experience with LLMs, Prompt Engineering, Fine-Tuning, RAG, Embeddings.
• Knowledge of frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar.

Agentic AI

• Experience building multi-agent systems and autonomous workflows.
• Understanding of planning, memory, reasoning, tool calling, and agent orchestration.

Data Science & Machine Learning

• Strong knowledge of Machine Learning, Deep Learning, NLP, and Statistical Modeling.
• Experience with Python, Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
• Data visualization using Power BI, Tableau, Matplotlib, or Seaborn.

Data Engineering

• Experience with SQL, NoSQL databases, ETL pipelines, and vector databases.
• Knowledge of data processing frameworks and cloud data platforms.

Cloud & Deployment

• Experience with cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud.
• Familiarity with Docker, Kubernetes, CI/CD, and MLOps practices.

• Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or related field.
• Experience with enterprise AI deployments and production-grade AI systems.
• Knowledge of AI governance, model evaluation, and responsible AI practices.
• Relevant certifications in AI/ML, cloud technologies, or data science.

• Knowledge of multimodal AI (text, image, audio, video).
• Experience with graph databases and knowledge graphs.
• Exposure to reinforcement learning and advanced AI agent architectures.

• Production-ready GenAI applications.
• Agentic AI workflows and autonomous agents.
• Predictive analytics and machine learning models.
• AI-powered business automation solutions.
• Scalable and secure AI deployments.