What are the responsibilities and job description for the Data Architect - MLops position at Hexaware Technologies?
Company Description
Hexaware Technologies is a global technology and business process services leader, home to over 31,600 professionals who collaborate to drive transformation for enterprises worldwide. With 58 offices across 28 countries, Hexaware specializes in enabling digital transformation with speed, scalability, and a commitment to customer success. The company fosters a people-first culture rooted in diversity, inclusivity, and continuous learning, providing employees with opportunities for growth and innovation. Hexaware strives to make a positive impact through technology, empowering clients and shaping a brighter digital future. Learn more about joining our vibrant team and making a difference at www.hexaware.com.
Role Description
Hexaware Technologies is seeking a Data Architect with expertise in MLops for a full-time, on-site role located in Reston, VA. The Data Architect will design, develop, and optimize robust data architectures for machine learning operations (MLops). Responsibilities include implementing data governance practices, developing data models, managing data warehousing frameworks, and overseeing Extract, Transform, Load (ETL) processes. This role requires close collaboration with cross-functional teams to deliver scalable data solutions that align with business goals and technical requirements.
Job Description
Qualifications
Education & Experience
Bachelor’s degree in computer science or related field required, master’s degree preferred.
12 years of progressive hands‑on experience in application development, analysis, engineering, solution architecture, and technical leadership.
Minimum 5 years of experience as a solution architect working with AWS or Azure.
Experience with Architecture principles and the TOGAF framework is a plus.
AWS Professional Certification preferred; AWS or Azure Architecture Associate Certification required.
CISSP or equivalent security certification is a plus.
Technical Expertise
Enterprise Data & Cloud Architecture
Expertise managing Enterprise Data Platforms including Data Lakes, Data Warehouses, and Data Marts.
Experience with real-time and near real-time data streaming platforms.
Expertise with relational, semi-structured, and unstructured databases.
Strong proficiency with Python or Java.
Experience designing large-scale APIs, microservices, and distributed streaming-based solutions.
Skilled in supporting and managing large, complex, and geographically distributed cloud environments.
Strong background in risk assessment, control design, gap remediation, and impact analysis.
MLOps Expertise
Extensive experience with MLOps frameworks and enterprise ML lifecycle automation, including:
ML Lifecycle Architecture & Automation
Designing and implementing end‑to‑end MLOps pipelines: data ingestion, feature engineering, model training, tuning, evaluation, versioning, CI/CD for ML, approvals, and automated deployment.
Establishing model governance, including lineage, auditability, explainability, data validation, and responsible AI controls.
Model Deployment, Serving & Monitoring
Designing microservice-based ML inference architectures using EKS/ECS, Lambda, Step Functions, and event-driven patterns.
Implementing advanced model monitoring:
drift detection
data quality checks
outlier detection
performance degradation alerts
CloudWatch / Open Telemetry observability pipelines
CI/CD for ML (MLOps)
Building automated ML pipelines using Code Pipeline, Bitbucket Pipelines, GitHub Actions, Jenkins, etc., integrated with container registries and SageMaker.
Defining enterprise patterns for ML environment standardization, reproducibility, and secure deployment.
Microservice Orchestration & Cloud-Native Engineering
Experience with orchestrating microservices using AWS ECS, EKS, Fargate, Lambda, EventBridge, App Mesh, and Step Functions.
Implementing service mesh patterns for service discovery, traffic routing, observability, and zero-trust communication.
Designing event-driven architectures leveraging Kinesis, SNS/SQS, and Lambda.
Experience building highly available, resilient, fault-tolerant cloud architectures.
AWS & Cloud Infrastructure Expertise
Proficiency with RDS PostgreSQL, Aurora, DynamoDB, and other AWS data services.
Experience with AWS VPC design, IAM, CloudFormation, AMIs, multi-account strategy, and landing zone architecture.
Strong knowledge of AWS services such as ELB, ElastiCache, CloudWatch, CloudTrail, S3, Lambda, Kinesis, App Mesh.
Experience designing cloud logging, alerting, and observability frameworks.
Expertise in AWS cloud security services and designing secure-by-default architectures.
Experience with Jenkins, GitHub, Bitbucket, and Docker in DevOps workflows.