The Role
Designs and builds secure, production-grade GenAI and agentic AI applications using Python, LLMs, AWS, and Databricks. Develops scalable AI pipelines, integrates data and cloud services, applies RAG, vector databases, model evaluation, and guardrails, and creates cost-effective solutions. The engineer researches emerging tools, improves application infrastructure and performance, works independently, guides team members, and delivers under tight timelines.
Summary Generated by Built In
DATAECONOMY is one of the fastest-growing Data & Analytics company with global presence. We are well-differentiated and are known for our Thought leadership, out-of-the-box products, cutting-edge solutions, accelerators, innovative use cases, and cost-effective service offerings.
We offer products and solutions in Cloud, Data Engineering, Data Governance, AI/ML, DevOps and Blockchain to large corporates across the globe. Strategic Partners with AWS, Collibra, cloudera, neo4j, DataRobot, Global IDs, tableau, MuleSoft and Talend.
GenAI/Agentic
AI Engineer - AWS
Boston, MA/ Charlotte, NC
Boston, MA/ Charlotte, NC
Full-time
Education and experience Qualification
· Bachelor's degree in computer science, Information Systems, or equivalent education or work experience.
· Around 5+ years of total experience and 2+ years as GenAI/Agentic AI engineer using cloud technologies.
· Any AWS and/or Databricks certification will be a plus.
Roles & Responsibilities
· Should be able to work independently with minimal directions and guide the team.
· Researching, learning, and applying new tools and techniques rapidly and suggesting new concepts to improve performance.
· Recognize the current application infrastructure and suggest new concepts to improve performance.
· Design and build production grade and secure GenAI and Agentic AI applications.
· Produce reusable, efficient, and scalable programs, and also cost-effective strategies.
· Develop AI pipelines and integrate with the data in Databricks and leverage different AWS services, including S3, EC2, API Gateway, Bedrock and Lambda.
· Comfortable to work on tight timelines, when required.
Skill Sets Required
· Good decision-making and problem-solving skills.
· Strong expertise in Python programming with specialization using various GenAI and Agentic libraries (LangChain, LangGraph, Pydantic, Strands, CrewAi etc.,).
· Familiarity of Databricks fundamentals/architecture and AWS Bedrock, EC2, Postgress and other key services
· Solid and proven experience in building GenAI and Agentic AI applications using LLM.
· Solid knowledge of GenAI components such as RAG, Vector Database, Model selection, Model evaluations, Guardrails etc.,
· Good Understanding of GenAI/Agentic AI model architecture best practices
· Hands-on experience in different domains, like database architecture, artificial intelligence, advanced analytics, big data, etc.
Nice to have Skill Sets
· Knowledge of Databricks, Apache Airflow.
· Knowledge of Machine Learning.
· Solid knowledge on CI/CD pipelines in AWS technologies.
· Finance and Compliance domain knowledge.
Education and experience Qualification
· Bachelor's degree in computer science, Information Systems, or equivalent education or work experience.
· Around 5+ years of total experience and 2+ years as GenAI/Agentic AI engineer using cloud technologies.
· Any AWS and/or Databricks certification will be a plus.
Roles & Responsibilities
· Should be able to work independently with minimal directions and guide the team.
· Researching, learning, and applying new tools and techniques rapidly and suggesting new concepts to improve performance.
· Recognize the current application infrastructure and suggest new concepts to improve performance.
· Design and build production grade and secure GenAI and Agentic AI applications.
· Produce reusable, efficient, and scalable programs, and also cost-effective strategies.
· Develop AI pipelines and integrate with the data in Databricks and leverage different AWS services, including S3, EC2, API Gateway, Bedrock and Lambda.
· Comfortable to work on tight timelines, when required.
Skill Sets Required
· Good decision-making and problem-solving skills.
· Strong expertise in Python programming with specialization using various GenAI and Agentic libraries (LangChain, LangGraph, Pydantic, Strands, CrewAi etc.,).
· Familiarity of Databricks fundamentals/architecture and AWS Bedrock, EC2, Postgress and other key services
· Solid and proven experience in building GenAI and Agentic AI applications using LLM.
· Solid knowledge of GenAI components such as RAG, Vector Database, Model selection, Model evaluations, Guardrails etc.,
· Good Understanding of GenAI/Agentic AI model architecture best practices
· Hands-on experience in different domains, like database architecture, artificial intelligence, advanced analytics, big data, etc.
Nice to have Skill Sets
· Knowledge of Databricks, Apache Airflow.
· Knowledge of Machine Learning.
· Solid knowledge on CI/CD pipelines in AWS technologies.
· Finance and Compliance domain knowledge.
Requirements
Skill Sets Required
· Good decision-making and problem-solving skills.
· Strong expertise in Python programming with specialization using various GenAI and Agentic libraries (LangChain, LangGraph, Pydantic, Strands, CrewAi etc.,).
· Familiarity of Databricks fundamentals/architecture and AWS Bedrock, EC2, Postgress and other key services
· Solid and proven experience in building GenAI and Agentic AI applications using LLM.
· Solid knowledge of GenAI components such as RAG, Vector Database, Model selection, Model evaluations, Guardrails etc.,
· Good Understanding of GenAI/Agentic AI model architecture best practices
· Hands-on experience in different domains, like database architecture, artificial intelligence, advanced analytics, big data, etc.
· Good decision-making and problem-solving skills.
· Strong expertise in Python programming with specialization using various GenAI and Agentic libraries (LangChain, LangGraph, Pydantic, Strands, CrewAi etc.,).
· Familiarity of Databricks fundamentals/architecture and AWS Bedrock, EC2, Postgress and other key services
· Solid and proven experience in building GenAI and Agentic AI applications using LLM.
· Solid knowledge of GenAI components such as RAG, Vector Database, Model selection, Model evaluations, Guardrails etc.,
· Good Understanding of GenAI/Agentic AI model architecture best practices
· Hands-on experience in different domains, like database architecture, artificial intelligence, advanced analytics, big data, etc.
Benefits
Standard fulltime benefits
Skills Required
- Bachelor's degree in Computer Science, Information Systems, or equivalent education or work experience
- Around 5+ years of total professional experience
- At least 2 years of experience as a GenAI or Agentic AI Engineer using cloud technologies
- Strong Python programming expertise with GenAI and agentic libraries, including LangChain, LangGraph, Pydantic, Strands, and CrewAI
- Familiarity with Databricks fundamentals and architecture
- Familiarity with AWS Bedrock, EC2, PostgreSQL, and other key AWS services
- Proven experience building GenAI and agentic AI applications using LLMs
- Knowledge of RAG, vector databases, model selection, model evaluation, and guardrails
- Understanding of GenAI and agentic AI model architecture best practices
- Hands-on experience with database architecture, artificial intelligence, advanced analytics, or big data
- AWS and/or Databricks certification
- Knowledge of Apache Airflow
- Knowledge of machine learning
- Knowledge of CI/CD pipelines in AWS technologies
- Finance and compliance domain knowledge
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
DATAECONOMY is a global, cloud-first data and AI consultancy delivering enterprise-grade solutions through an innovative intellectual-property suite. Its work spans data and BI platform modernization, self-service AI, data mesh and fabric, master data management, governance, cloud enablement, digital engineering, knowledge graphs, and machine lakes supporting cybersecurity and financial-crime use cases for enterprise clients.
