Agentic AI Solutions Architect Manager

Full Description

Job Location: Remote, PK

Department: Technology

Type: Regular, Full-Time

Working hours: 6 pm – 3 am (US Hours)

Vision: Transforms students’ lives through Innovating workforce development models addressing skillset gaps

Mission: Transforms students’ lives by curating university programs and mapping them to certifications aligned to the emerging job market through apprenticeship and upskillingprograms

At EdTech Ventures, we are committed to upholding the following core values:

Passion |Respect | Accountability | Innovation | Speed | Execution [ PRAISE ]

Overview:

We are looking for a Agentic AI Solutions Architect with deep expertise in AI systems and enterprise architecture to lead the design and development of the AI-enhanced QuickStart RAD platform.

This role requires a balance of technical vision, hands-on experience, and leadership to build an agile, scalable, and intelligent architecture that supports fast-paced innovation.

You’ll work across AI/ML, backend, frontend, data pipelines, DevOps, and cloud to create a seamless platform that empowers rapid delivery of AI-enabled business applications.

Responsibilities (include, but are not limited to):

Architectural Leadership

• Lead the technical architecture and development of AI-native applications, leveraging data engineering and data science expertise to drive innovation and scalability.

• Oversee the design, development, and deployment of agentic AI systems, ensuring seamless integration with existing infrastructure and supporting business growth.

• Architect AI/ML pipelines, APIs, data flows, model-serving/inference systems, and integrations with low-code/no-code platforms.

• Establish architectural standards for AI model integration, data versioning, performance optimisation, observability, and security.

AI & Platform Integration

• Drive technical innovation by ensuring system scalability, reliability, and performance while delivering meaningful business value through AI-powered applications.

• Select, integrate, and optimize AI/ML platforms and frameworks (e.g., TensorFlow, PyTorch, Hugging Face, OpenAI, LangChain) within the development ecosystem.

• Design intelligent components such as chatbots, recommender systems, NLP engines, and generative AI assistants for internal and external use cases.

• Ensure AI models are deployable, monitorable, and reusable through microservices, containerized components, and API-driven architecture.

Technology Strategy

• Guide platform evolution by evaluating and adopting emerging AI tools, including LLMs, AutoML platforms, AI agents, and vector databases.

• Provide architectural direction for cloud-native deployments (AWS, GCP, Azure), edge AI use cases, and scalable model-hosting solutions.

• Oversee the selection and integration of RAD/low-code development tools such as Retool, Mendix, OutSystems, or internally developed frameworks.

Team & Execution Oversight

• Lead and mentor engineering, data science, MLOps, and DevOps teams across architecture, development, and delivery functions.

• Conduct technical design reviews, code reviews, and enforce best practices in security, performance, reliability, and scalability.

• Partner closely with product and business stakeholders to translate AI capabilities into practical, high-impact business features and solutions.

Minimum Qualifications

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, AI, Software Engineering, or a related discipline.

• 10+ years of experience in software engineering, with at least 4–5 years in solutions architecture or technical architecture leadership roles.

Technical Skills

• Strong experience architecting, deploying, and scaling AI/ML models in production environments.

• Hands-on expertise in MLOps, model versioning, vector search technologies (e.g., Pinecone, Weaviate), and building APIs (REST, GraphQL).

• Proficiency with cloud AI platforms such as AWS SageMaker, Azure ML, or GCP Vertex AI.

• Deep understanding of modern architectural patterns: microservices, containerization (Docker/Kubernetes), serverless computing, and event-driven systems.

AI Tools & Frameworks

• Expertise with LangChain, OpenAI API, and RAG pipelines.

• Proficiency in PyTorch, TensorFlow, scikit-learn, and classical ML frameworks.

• Experience with vector databases such as FAISS, Milvus, Weaviate, or Pinecone.

• Familiarity with ML orchestration tools like Kubeflow, MLflow, and Apache Airflow.

Soft Skills & Leadership

• Strategic thinker with the ability to balance velocity and scalability in AI product delivery.

• Strong communication, collaboration, and leadership skills.

• Experience leading distributed and cross-functional teams.

• Ability to evaluate technical trade-offs, prioritize effectively, and influence decision-making across the organization.

Competency Identifiers

Technical Expertise in AI & Data Engineering

• Proven ability to design and implement AI systems, data pipelines, and scalable architectures.

• Proficiency in Python, Java, or similar languages and relevant development frameworks.

System Design & Architecture

• Skilled in designing scalable, secure, and efficient systems integrating AI and data engineering components.

• Strong understanding of microservices architecture and cloud computing.

• Proficient with cloud and big data tools such as S3, BigQuery, Snowflake, Redshift, and Databricks.

Data Science & Machine Learning

• Strong understanding of data science fundamentals, machine learning algorithms, and statistical modeling.

• Demonstrated ability to apply ML/AI concepts to solve real-world business problems.

Leadership & Collaboration

• Ability to lead cross-functional teams and communicate complex technical concepts to non-technical stakeholders.

• Commitment to staying current with emerging technologies in AI, data engineering, and cloud ecosystems.

Delivery & Execution Excellence

• Owns end-to-end technical delivery and system architecture.

• Enforces architectural governance, documentation standards, and best practices.

• Implements robust CI/CD, DevSecOps, and observability practices.

• Proactively addresses technical debt, scalability challenges, and system reliability.

Key Performance Indicators (KPIs)

• System Scalability & Performance

Metrics include latency, throughput, error rates, and ability to handle growth in traffic and data volume.

• AI Model Accuracy & Reliability

Measures include model accuracy, precision, recall, consistency, and resilience in production environments.

• Data Quality & Integrity

Metrics include data completeness, freshness, accuracy, and consistency across pipelines and systems.

• Time-to-Market & Deployment Frequency

Focus on reducing development cycle time and increasing stable deployment frequency for AI features and models.

• Business Value & ROI

Metrics include revenue growth, cost savings, operational efficiency, and customer satisfaction enabled by AI initiatives.

Job Type: Full-time

Work Location: Remote