Senior AI/ML Data Engineer – AWS & Snowflake
Role summary
We are seeking a Senior AI/ML Data Engineer with expertise in AWS and Snowflake to design and build scalable AI-driven solutions. This role focuses on end-to-end AI/ML pipelines, LLM development, and AI agent creation within a modern cloud environment. Responsibilities include architecting solutions on AWS, integrating with Snowflake for data platforms, and ensuring robust data governance. The ideal candidate will have 4-6+ years of experience deploying production-grade AI/ML solutions, proficiency in Python, and strong skills in ML frameworks and CI/CD practices. This position requires translating business needs into technical solutions and maintaining high standards for data quality and security.
We are looking for a
Senior AI/ML Engineer
to design and build scalable AI-driven solutions in a modern cloud environment. This role requires strong hands-on experience with
AWS AI/ML services, Large Language Models (LLMs), and Snowflake
, along with the ability to develop intelligent systems that integrate seamlessly with enterprise data platforms.
You will work on end-to-end AI/ML pipelines, develop AI agents, and integrate advanced machine learning capabilities into business workflows. The role also involves working closely with stakeholders to translate business requirements into scalable technical solutions and ensuring high-quality, secure, and governed data practices.
Key Responsibilities
AI/ML Solution Development
- Design and implement end-to-end
AI/ML pipelines
using AWS services
- Develop and deploy
LLMs
for real-world business use cases
- Build and optimize
AI agents
using AWS technologies (Bedrock, SageMaker, Lambda)
- Implement NLP, computer vision, and multi-modal AI solutions
- Develop scalable workflows for
batch and real-time inference
AWS Cloud Architecture
- Architect and deploy AI/ML solutions using AWS ecosystem
- Utilize
SageMaker
for training, tuning, and deployment
- Build serverless solutions using
Lambda, API Gateway, Step Functions
- Integrate foundation models using
AWS Bedrock
- Design data platforms using
S3, Glue, Athena
Snowflake & Data Platform Integration
- Work with
Snowflake
as part of the enterprise data platform
- Design and optimize data pipelines integrating Snowflake with AI/ML workflows
- Support data modeling, transformation, and query optimization in Snowflake
- Ensure efficient data access patterns for AI/ML use cases
AI Agent Development
- Build intelligent, multi-modal AI agents
- Develop conversational interfaces using AWS tools (Lex, Connect)
- Implement orchestration and workflow automation for AI systems
- Monitor and optimize agent performance
Data Classification & Quality
- Develop automated
data classification and profiling systems
- Implement data quality monitoring and validation frameworks
- Build data lineage and metadata management solutions
- Ensure compliance with data governance and regulatory standards
LLM Integration & Optimization
- Integrate LLMs into enterprise applications
- Implement prompt engineering and fine-tuning strategies
- Build
RAG (Retrieval-Augmented Generation)
systems
- Optimize performance for cost, latency, and accuracy
DevOps & CI/CD
- Build CI/CD pipelines using
GitHub Actions
- Implement infrastructure automation and deployment workflows
- Manage secrets securely using
AWS Secrets Manager
- Develop automated testing frameworks for ML pipelines
- Support GitOps and environment management across environments
Required Qualifications
Education & Experience
- Master’s degree in Computer Science, Data Science, AI/ML, or related field
- 4–6+ years of hands-on experience
in AI/ML engineering and cloud environments
- Proven experience deploying
production-grade AI/ML solutions
Core Technical Skills
- Strong expertise in
AWS AI/ML services
(SageMaker, Bedrock, Lambda, S3, Glue, Athena, Step Functions)
- Hands-on experience with
Snowflake
in data engineering or ML workflows
- Experience with
LLM development, fine-tuning, and deployment
- Proficiency in
Python
(experience with R/Java/Scala is a plus)
- Experience with ML frameworks:
TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain
- Strong knowledge of
data classification, metadata management, and data quality frameworks
Specialized AI/ML Skills
- Experience with foundation models and transfer learning
- Knowledge of reinforcement learning and multi-agent systems
- Experience with NLP and/or computer vision
- Understanding of responsible AI, bias detection, and model ethics
- Familiarity with MLOps and model lifecycle management
Cloud & Infrastructure
- Experience with
Infrastructure as Code
(Terraform, CloudFormation)
- Knowledge of
Docker, Kubernetes, ECS/EKS
- Experience building CI/CD pipelines for ML workflows
- Familiarity with monitoring tools (CloudWatch, X-Ray)
- Understanding of cloud security and compliance best practices
DevOps & Security
- Strong experience with
GitHub (version control, workflows, PRs)
- Hands-on experience with
GitHub Actions
- Familiarity with secure SDLC and automated security practices
- Knowledge of IAM, KMS, and other AWS security services
Key Competencies
- Strong problem-solving and analytical thinking
- Excellent communication skills across technical and business teams
- High attention to detail and documentation quality
- Ability to work in fast-paced, evolving environments
- Proactive mindset and ownership of deliverables
- Strong collaboration skills in distributed teams