Key Facts

Remote From:

Full time

Senior (5-10 years)

English

Hard Skills

Google Cloud Platform (GCP) Spring AOP Python (Programming Language) Deep Learning Business Strategies Kubernetes General Security Proof Of Concept (POC) Development Basic Military Training Containerization Application Programming Interface (API) Software Architecture Kubeflow Application Programming Interface (API) RabbitMQ Applications Architecture Data Architecture Code Analysis Apache Airflow Reliability Engineering Apache Kafka Docker (Software) Machine Learning MLflow Lifecycle Management Architectural Design Systems Integration Apache Spark Scikit-Learn (Python Package) Microservices Ethical AI CI/CD Dask (Software) PyTorch (Machine Learning Library) Performance Systems Analysis Azure Machine Learning Domain Driven Design Distributed Computing Data Strategy Scalability AWS SageMaker MLOps (Machine Learning Operations) TensorFlow DevOps Risk Mitigation Stakeholder Communications Internal Documentation

Other Skills

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Governance
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Team Leadership
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Collaboration
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Communication
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Problem Solving

Roles & Responsibilities

8+ years in ML/DL, data science, or AI-related roles
Proficiency in Python and ML frameworks; experience with distributed computing (Spark, Dask)
Cloud-native ML deployment experience on AWS (SageMaker), Azure (ML), or GCP (Vertex AI)
Strong knowledge of architecture patterns for large-scale systems, microservices, Kubernetes, Docker, and DevOps practices

Requirements:

Design and review end-to-end ML pipelines; architect scalable models; define standards for MLOps, model governance, and responsible AI
Own technical design for backend systems (Java/Spring Boot); create solution blueprints, architecture documents, and design patterns; guide microservices, containerization, API design, and DevOps
Analyze requirements and design cross-platform technical solutions; create architecture roadmaps for applications, integrations, and cloud infrastructure; lead POCs and tech evaluations
Collaborate with stakeholders across data engineering, product, and infrastructure teams to align technical goals with business needs and ensure security, scalability, and reliability

Job description

This is a remote position.

1. Job Title: AI/ML Architect

Key Responsibilities: Design and review machine learning architecture pipelines end-to-end. Architect scalable models using tools like TensorFlow, PyTorch, Scikit-learn. Drive data strategy, feature stores, model lifecycle (training, deployment, monitoring). Collaborate with data engineering and product teams to align technical goals. Define standards for MLOps, model governance, and responsible AI.

Must-Have Skills: 8+ years in ML/DL, data science, or AI-related roles. Expertise in Python, ML frameworks, distributed computing (Spark, Dask). Cloud-native ML deployment on AWS SageMaker, Azure ML, GCP Vertex AI. Knowledge of Kubeflow, MLFlow, Airflow, Docker/K8s for ML pipelines. Strong grasp of ML lifecycle management, monitoring, and retraining.

2. Job Title: Java Architect

Key Responsibilities: Own the technical design for backend systems built on Java / Spring Boot. Create solution blueprints, architecture documents, and design patterns. Guide teams on microservices, containerization, API design, and DevOps. Conduct code reviews, architecture assessments, and risk mitigation. Collaborate with product and infrastructure teams for seamless delivery.

Must-Have Skills: Proficiency in Java 11+/Spring Boot, RESTful APIs, ORM frameworks. Experience with Kubernetes, Docker, messaging systems (Kafka, RabbitMQ). Strong grasp of cloud-native development (AWS, Azure, GCP). In-depth understanding of microservices, DDD, clean architecture. Performance tuning, scalability, and reliability design.

3. Job Title: Solution Architect

Key Responsibilities: Analyze requirements and design technical solutions across platforms. Create architecture roadmaps for applications, integrations, and cloud infrastructure. Lead POCs, architecture evaluations, and tech selection. Align solutions with enterprise architecture standards and security best practices. Act as a technical bridge between stakeholders, developers, and leadership.

Must-Have Skills: Strong experience in application architecture, APIs, and system integration. Familiarity with cloud platforms (Azure, AWS, GCP). Solid understanding of data architecture, DevOps practices, CI/CD pipelines. Experience with design patterns, scalability, and resilience planning. Great communication, documentation, and stakeholder management skills.