About The Role
We are looking for a curious and motivated analyst/engineer to join our Data & AI team. You will work on real-world data pipelines, build and evaluate ML models, and deploy intelligent agentic workflows powered by modern LLM tooling. This is a hands-on, fast-paced role.
Roles and responsibilities
- Effective knowledge sharing to researchers, coupled with Gen AI technology upgrading and mentoring
- Collect, clean, and transform structured/unstructured data for analysis and model training.
- Build and fine-tune ML models (classification, regression, clustering, NLP) using Python.
- Develop and test agentic AI workflows using LLM APIs (Claude, GPT-4o, Gemini) with tool use, memory, and planning.
- Integrate agents with external tools — search, databases, APIs — using MCP or function calling.
- Create PowerBi dashboards, visualisations, and reports to communicate insights to stakeholders.
- Evaluate and benchmark LLM outputs; implement prompt engineering and RAG pipelines.
- Assist in deploying models and agentic applications to Azure cloud or on-premise environments.
- Collaborate with cross-functional teams to understand business requirements and deliver AI-driven solutions.
Educational Qualification
- M.E/M.Tech : Multi-disciplinary Engg. domain with Data Science, Artificial Intelligence, Statistics, or a related field.
- Relevant certifications (Azure : Data Analytics, ML Specialty, DeepLearning.AI, etc.) are a strong plus.
- A GitHub portfolio demonstrating hands-on projects is strongly preferred over certifications alone.
Nice to Have
- Experience building end-to-end agentic applications [ReAct agent, multi-agent system using CrewAI or AutoGen]
- Familiarity with MCP server development or custom tool integrations for LLMs.
- Contributions to open-source AI/ML projects on GitHub.
- Knowledge of LLM evaluation frameworks such as RAG
- Exposure to MLOps practices: experiment tracking, model versioning, and CI/CD pipelines.
Soft skills
- Strong analytical thinking and problem-solving ability.
- Eagerness to learn rapidly evolving AI/ML technologies and frameworks.
- Clear written and verbal communication for presenting findings to non-technical stakeholders.
- Ability to work independently and collaboratively in an agile team environment.
- Intellectual curiosity and a builder's mindset — you like shipping things, not just experimenting.
