Principal Data Scientist

Principal Data Scientist    

Key Responsibilities

• AI & ML Strategy : Advise, design, and execute ML- and LLM-driven transformations of business processes with clear, measurable outcomes.
• End-to-End AI Solutions : Build and operationalize ML/LLM solutions including data pipelines, feature engineering, modelling, APIs, deployment, and monitoring.
• AI Platform Engineering : Architect and scale distributed AI execution platforms capable of running thousands of concurrent models or agents
• Agent & Model Lifecycle Management : Design services and APIs for agent/model registration, versioning, execution, and monitoring.
• Scalability & Performance : Optimize distributed systems and microservices for low latency, high throughput, and cost efficiency.
• Governance & Reliability : Establish standards for validation, monitoring, drift detection, bias mitigation, safety guardrails, and compliance.
• LLMOps / MLOps : Implement CI/CD, automated evaluation, observability, and rollback strategies for ML and LLM systems.
• Cross-Functional Leadership : Partner with Product, Engineering, and Business teams to identify opportunities and operationalize AI at scale.
• Mentorship & Best Practices : Mentor engineers and data scientists; define best practices for AI platform, ML, and LLM development.

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Required Skills & Experience

• 12 + years of experience in AI/ML systems, or/and AI platform engineering.
• Expert proficiency in Python or similar programming language
• Deep experience building and scaling distributed systems (e.g., Kafka, stream processing, HPC or large-scale compute frameworks).
• Advanced knowledge of machine learning : regression, classification, clustering, tree-based models, ensembles, Bayesian/Markov methods.
• Hands-on experience with Large Language Models (GPT, BERT, or similar), including prompt engineering, RAG pipelines, and evaluation .
• Strong NLP expertise : text classification, summarization, question answering, and entity recognition.
• Experience designing end-to-end AI pipelines : data ingestion, training, deployment, monitoring, and feedback loops.
• Strong knowledge of cloud platforms (Azure or AWS), Kubernetes , autoscaling
• Experience building high-throughput APIs (REST/gRPC) and platform service interfaces.
• Strong understanding of observability (logging, metrics, tracing), security, and performance optimization

Familiarity with agentic frameworks, large language models (LLMs), agent protocols (MCP, A2A) and their unique deployment challenges will be plus

Key Responsibilities

• AI & ML Strategy : Advise, design, and execute ML- and LLM-driven transformations of business processes with clear, measurable outcomes.
• End-to-End AI Solutions : Build and operationalize ML/LLM solutions including data pipelines, feature engineering, modelling, APIs, deployment, and monitoring.
• AI Platform Engineering : Architect and scale distributed AI execution platforms capable of running thousands of concurrent models or agents
• Agent & Model Lifecycle Management : Design services and APIs for agent/model registration, versioning, execution, and monitoring.
• Scalability & Performance : Optimize distributed systems and microservices for low latency, high throughput, and cost efficiency.
• Governance & Reliability : Establish standards for validation, monitoring, drift detection, bias mitigation, safety guardrails, and compliance.
• LLMOps / MLOps : Implement CI/CD, automated evaluation, observability, and rollback strategies for ML and LLM systems.
• Cross-Functional Leadership : Partner with Product, Engineering, and Business teams to identify opportunities and operationalize AI at scale.
• Mentorship & Best Practices : Mentor engineers and data scientists; define best practices for AI platform, ML, and LLM development.

• ---

• Required Skills & Experience

• 12+ years of experience in AI/ML systems, or/and AI platform engineering.
• Expert proficiency in Python or similar programming language
• Deep experience building and scaling distributed systems (e.g., Kafka, stream processing, HPC or large-scale compute frameworks).
• Advanced knowledge of machine learning : regression, classification, clustering, tree-based models, ensembles, Bayesian/Markov methods.
• Hands-on experience with Large Language Models (GPT, BERT, or similar), including prompt engineering, RAG pipelines, and evaluation .
• Strong NLP expertise : text classification, summarization, question answering, and entity recognition.
• Experience designing end-to-end AI pipelines : data ingestion, training, deployment, monitoring, and feedback loops.
• Strong knowledge of cloud platforms (Azure or AWS), Kubernetes , autoscaling
• Experience building high-throughput APIs (REST/gRPC) and platform service interfaces.
• Strong understanding of observability (logging, metrics, tracing), security, and performance optimization

Familiarity with agentic frameworks, large language models (LLMs), agent protocols (MCP, A2A) and their unique deployment challenges will be plus