Senior Data Scientist - ML, Python

Job Summary

Preferred Skills and Attributes: Experience: 8+ years total, with deep exposure to enterprise AI use cases
Experience building multi-agent autonomous systems with reasoning, planning, and tool use
Knowledge of graph-based workflows and orchestration frameworks
Experience in fine-tuning LLMs and working with open-source models (Llama, Mistral, etc.)
Familiarity with evaluation frameworks (prompt testing, hallucination detection, safety benchmarking)
Experience with real-time streaming data systems and event-driven architectures
Exposure to Responsible AI practices, governance, and model risk management
Experience in data engineering pipelines (Spark, Databricks) is a plus
Strong collaboration and stakeholder management skills in enterprise environments

Key Responsibilities

1. Design And Implement Advanced Machine Learning Algorithms Using Tensorflow And Pytorch To Extract Insights From Large Datasets, Delivering Customized Analytical Reports Tailored To The Needs Of Colleagues, Clients, And Stakeholders.

2. Leverage Python And Sql To Develop And Optimize Data Models That Address Specific Organizational Challenges, Ensuring Effective Integration And Management Of Data.

3. Analyze And Mine Large Datasets To Identify Trends And Patterns, Utilizing Advanced Analytics Techniques To Interpret Findings And Provide Actionable Recommendations Based On Experimental Results.

4. Collaborate With Cross-Functional Teams To Identify Opportunities For Utilizing Data Insights To Formulate Strategic Business Solutions That Enhance Operational Efficiency And Product Offerings.

5. Create Comprehensive Visualizations And Reports Using Data Visualization Tools To Communicate Complex Analysis And Results Clearly, Enabling Informed Decision-Making For Customers And Stakeholders.

Skill Requirements

Experience: 4–7+ years in AI/ML engineering, with at least 2+ years in Generative AI / LLM-based systems
Strong proficiency in Python and ML frameworks (Py Torch, Tensor Flow, Scikit-learn)
Hands-on experience building Agentic AI systems (multi-agent frameworks, autonomous workflows, orchestration patterns)
Experience with LLM application frameworks such as Lang Chain, Semantic Kernel, Auto Gen, or equivalent
Solid understanding of RAG (Retrieval-Augmented Generation), vector databases (FAISS, Pinecone, Azure AI Search)
Experience designing prompt engineering strategies, evaluation pipelines, and guardrails
Strong skills in API development and microservices architecture (FastAPI, Flask)
Familiarity with cloud platforms (Azure preferred) including Azure OpenAI, AI Studio, Functions, Kubernetes
Experience with CI/CD, MLOps, and deploying ML/AI solutions at scale
Strong problem-solving skills and ability to design end-to-end intelligent systems

Other Requirements

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