Senior Data Scientist
Job Description
Job Description
Senior AI Engineer
Education
Senior AI Engineer
Education
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field
- Strong academic foundation in statistics, probability, and LLM and Agentic AI and AWS Bedrock Experience
- Optional skills Machine Learning and Neural networks
- 6–8 years of professional experience in LLM, Agentic AI
- All aspects of BEDROCK, use of security APIs from AWS, Dynamo DB, Experience in Claude LLMs
- Experience working on end-to-end Bedrock workflows
- Ability to translate business and product requirements into data-driven solutions
- Hands-on experience developing LLM-powered applications using Amazon Bedrock or similar platforms
- Experience building or contributing to Retrieval-Augmented Generation (RAG) pipelines, including:
- Document ingestion and chunking
- Embedding generation
- Similarity search using vector databases
- Practical knowledge of prompt engineering, prompt tuning, and output evaluation
- Understanding of common LLM failure modes such as hallucinations and grounding issues
- Ability to evaluate LLM responses for accuracy, relevance, and safety
- Exposure to Agentic AI concepts and frameworks such as AgentCore
- Experience implementing:
- Simple Autonomous agents or workflows
- Multi-step reasoning with predefined tools
- Familiarity with tool-calling, agent orchestration, and workflow automation
- Understanding the importance of:
- Guardrails
- Human-in-the-loop mechanisms
- Logging and observability for agent behavior
- Working knowledge of key AWS services, including:
- S3, Lambda, Redshift, IAM, S3, AWS WAF, Query handler , Lambda
- Strong Python programming skills and experience working with APIs
- Ability to work effectively in cross-functional teams (product, engineering, analytics)
- Willingness to learn quickly in a fast-evolving AI landscape ( flexibility and learning attitude )
- Comfortable taking technical guidance and implementing feedback
- Clear communication of findings, limitations, and model behavior to non-technical stakeholders
- Initial exposure to AI governance, compliance, or ethical AI practices
- Experience with monitoring LLM outputs and basic evaluation frameworks
- Certifications in AWS Bedrock Gen AI Developer Professiona, Machine Learning, or Data Science
More Info
Key Skills
AWS Bedrock
Embedding generation
Similarity search
Agentic AI
Claude LLMs
Document ingestion
prompt tuning
multi-step reasoning
AgentCore
Human-in-the-loop mechanisms
Simple Autonomous agents
vector databases
Guardrails
logging and observability
prompt engineering
tool-calling agent orchestration
workflow automation
output evaluation
About Company
Skills:
Ml, Power Bi, Tableau, Python, Sql, Data Modelling, Cspo, Agile, Csm, Generative AI, reporting governance, Ai, Data Validation, Performance Measurement, model governance, Product Management, LLMs, prompt engineering, AI agents, AI solution architecture, API integrations, data flows, RAG architectures, enterprise AI use cases
Skills:
Machine Learning, Sql, Databricks, Python, Pyomo, Statistics, MLflow, OR-Tools, Prescriptive Analytics, Optimization, Digital Twins, Industrial Data, Knowledge Graphs, Gurobi
Skills:
causal analysis , Machine Learning, Apis, Sql, Market Basket Analysis, Version Control, Dynamic Programming, Python, Forecasting, heuristics, Optimization, anomaly detection, Experiment design, Simulation, mathematical programming
Skills:
Machine Learning, Deep Learning, Nlp, MLops, Docker, Databricks, Kubernetes, Python, LangChain, Generative AI, LLMs, LLMOps, Azure OpenAI, Vector Databases, Agentic AI, Healthcare Analytics, RAG, Multimodal AI, Prompt Engineering, Knowledge Graphs
Skills:
model design , Machine Learning, Pyspark, Deep Learning, Tensorflow, Nlp, Pandas, Pytorch, Spark, Azure Data Lake, Databricks, Azure, Python, GenAI, scikit-learn, Hugging Face Transformers, Azure OpenAI Service, MLflow, Azure AI Foundry, prompt engineering, Llm, RAG, feature engineering