QUALIFICATIONS: 7+ years of experience building and delivering production-grade software, data, or AI solutions
2-3 years of hands-on experience in AI/ML systems, including agentic workflows, LLM integrations, or intelligent automation
Hands-on experience with AWS cloud services and cloud-native architectures
BS in Engineering, Computer Science, Data Science, or related field required; advanced degree preferred
TECHNICAL SKILLS:- Strong knowledge of software development methodologies (Agile, Waterfall) and modern engineering best practices
- Strong programming skills in languages such as Python, Java/J2EE, JavaScript, SQL, and modern frameworks
- Experience designing and building scalable systems, including APis, microservices, and distributed architectures
- Proficiency in CI/CD pipeline design, implementation, and maintenance, including automated testing, deployment, and release management Experience with Git-based workflows and GitHub-based automation, including GitHub Actions and GitHub agent capabilities
- Experience with AWS cloud services (e.g., EC2, S3, Lambda, RDS, EKS/ECS) and building cloud-native applications Understanding of cloud architecture patterns, including scalability, resiliency, and cost optimization
- Platform engineering experience, including building and maintaining reusable infrastructure, developer platforms, and self-service tooling (e.g.
- Infrastructure as Code using Terraform/CloudFormation)
- Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes)
- Proficiency in designing and implementing AI-powered systems, including LLM-based applications, agent frameworks, and workflow orchestration Experience developing, evaluating, and deploying machine learning models (supervised, unsupervised, or deep learning)
- Strong understanding of statistical analysis, experimentation, and model evaluation techniquey Experience with data processing and analysis tools (e.g., Pandas, Spark, or equivalent) Advanced proficiency in data modeling, data manipulation, and optimization techniques
- Strong nowledge of normalized and dimensional data modeling principles
- Solid understanding of multiple data storage systems (relational, NoSQL, and data lake architectures) Familiari y with LQps practices, including model lifecycle management, monitoring, and observability Knowledge of test-driven development and modern testing strategies across both data and application layers
- Strong understanding of prompt engineering, evaluation strategies, and reliability considerations for AI systems Strong research and problem-solving skills, including evaluating and applying emerging technologies
- Ability to design, document, and implement complex system and data components
- Strong communication skills, with the ability to work effectively across technical and non-technical audiences
ACCOUNTABILITIES: . . .
Design, develop, and deliver scalable solutions that integrate data, machine learning, an applicat on lo ic
Build and enhance AI-driven capabilities, including agentic workflows, LLM-based applications, and intelligent automation systems
Develop and deploy solutions on AWS, ensuring scalability, reliability, and cost efficiency
contribute to platform engineering efforts, including improving developer experience, deployment pipelines, and infrastructure reuse
Translate business problems into end-to-end solutions, including data exploration, feature engineering, modeling, and system integration
contribute to s,stern and solution architecture to ensure scalability, reliability, and maintainability
Partner ith product, engineering, and business stakeholders to define and deliver impactful solutions
Lead or contribute to moderately complex, cross-functional initiatives
Write and review detailed technical specifications for system and data components
Ensure adherence to best practices across coding, testing, data quality, and deployment
Resolve complex technical and data-related issues across systems and models
Mentor junior_ano_mid-level team members across development, data, and AI practices
Collaborate with internal and external technology resources to deliver solutions
Stay current_with e erging technologies in AI, cloud, and software systems, and recommend adoption where appropriate
Support continuous i provement_of development processes, data pipelines, and platform capabilities
Serve as e key technical contributor and escalation point within the team
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