Senior / Principal Data Scientist – ML, GenAI & Agentic AI
Experience & Qualifications
Education
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, AI/ML, or a related field
- PhD is a plus for Principal-level candidates, especially with applied research or publications in ML/AI
Core Experience
- 8–10+ years of hands-on experience in Data Science, Machine Learning, or Applied AI
- Proven experience delivering production-grade ML systems in large-scale or enterprise environments
- Strong background in statistical modeling, feature engineering, experimentation, and model evaluation
- Experience owning the full ML lifecycle: problem framing → modeling → deployment → monitoring → iteration
Machine Learning & MLOps
- Extensive experience with Amazon SageMaker for training, tuning, hosting, and managing ML models
- Deep understanding of supervised, unsupervised, and time-series modeling techniques
- Hands-on experience implementing CI/CD for ML, model versioning, and automated retraining pipelines
- Strong knowledge of Docker, Kubernetes, and cloud-native architectures
- Experience with monitoring model drift, data quality, and performance metrics in production
Generative AI & LLM Engineering
- Proven experience building LLM-powered applications using Amazon Bedrock
- Strong expertise in RAG architectures, including:
- Document ingestion and chunking strategies
- Embedding generation and tuning
- Vector databases and semantic search
- Advanced prompt engineering, prompt chaining, and response evaluation techniques
- Experience optimizing latency, cost, and accuracy of LLM workloads
- Hands-on with LLM evaluation frameworks, grounding methods, and hallucination mitigation
- Understanding of LLM safety, bias detection, and governance controls
Agentic AI & Autonomous Systems
- Experience designing and deploying Agentic AI systems using AgentCore or similar frameworks
- Ability to build multi-step reasoning agents with tool usage (APIs, databases, services)
- Experience designing multi-agent architectures for task planning, orchestration, and collaboration
- Strong understanding of agent memory, planning, feedback loops, and self-correction mechanisms
- Practical experience implementing guardrails, human-in-the-loop systems, observability, and traceability
Cloud Platform
- Strong experience with AWS services including:
- S3, Lambda, Redshift, IAM, API Gateway
- Event-driven and microservices-based architectures
Leadership & Collaboration
- Experience leading technical design discussions and influencing architecture decisions
- Proven ability to mentor junior data scientists and ML engineers
- Strong stakeholder communication skills—able to translate business problems into AI-driven solutions
- Experience working with product, engineering, security, and compliance teams
- Comfortable operating in ambiguous, fast-evolving AI environments
Nice to Have / Differentiators
- Experience with enterprise AI governance frameworks
- Background in decision intelligence, optimization, or reinforcement learning
- Contributions to open-source ML/AI projects or internal AI platforms
- Prior experience in regulated industries (FSI, Public Sector, Telecom)
- Exposure to cost optimization strategies for large-scale AI systems
Skills
Agentic AIArtificial Intelligence/Machine Learning
