Agentic AI Systems Architect

Job Location: Remore, 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 skill set gaps

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

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

P*assion |Respect | Accountability | Innovation | Speed | Execution [ PRAISE* ]

Overview:

We are looking for a Agentic AI Systems 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. This role will oversee the design, development, and deployment of agentic AI systems, ensuring seamless integration with existing infrastructure and driving business growth.

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• Architect AI/ML pipelines, APIs, data flow, model serving/inference systems, and integration with low-code/no-code layers.
• Establish architectural standards for AI model integration, data versioning, performance optimization, and security.

AI & Platform Integration

• Drive technical innovation, ensuring system scalability and performance, and delivering business value through AI-powered applications.
• Select and integrate AI/ML platforms (e.g., TensorFlow, PyTorch, Hugging Face, OpenAI, LangChain, etc.) into the RAD environment.
• Design intelligent components such as chatbots, recommender systems, NLP engines, or generative AI assistants for internal use.
• Ensure AI models are easily deployable, monitorable, and reusable via microservices or API-driven architecture.

Technology Strategy

• Guide platform evolution by evaluating emerging AI tools (e.g., LLMs, AutoML, AI agents, vector databases).
• Provide direction on cloud-native deployment (AWS/GCP/Azure), edge AI, and scalable model hosting.
• Oversee selection and adoption of RAD/low-code technologies (e.g., Retool, Mendix, OutSystems, custom internal frameworks).

Team & Execution Oversight

• Lead and mentor engineering, data science, and DevOps teams across architecture, coding, and delivery.
• Conduct design reviews, code reviews, and enforce best practices in security, performance, and scalability.
• Collaborate closely with product and business teams to translate AI capabilities into usable business features.

Minimum Qualifications:

Education & Experience

• Bachelor’s or Master’s in Computer Science, AI, Software Engineering, or related field.
• 10+ years of experience in software engineering, with at least 4–5 years in solution or technical architecture roles.

Technical Skills

• Strong experience in architecture and deploying AI/ML models in production environments.
• Hands-on knowledge of ML Ops, model versioning, vector search (e.g., Pinecone, Weaviate), and APIs (REST/GraphQL).
• Solid experience with cloud services for AI (AWS SageMaker, Azure ML, GCP Vertex AI, etc.).
• Proven experience with modern architecture patterns: microservices, containerization (Docker/Kubernetes), serverless, event-driven systems.

AI-Specific Tools & Frameworks

• LangChain, OpenAI API, RAG pipelines.
• PyTorch, TensorFlow, scikit-learn.
• Vector DBs: FAISS, Milvus, Weaviate, Pinecone.
• ML pipeline orchestration tools: Kubeflow, MLflow, Airflow.

Soft Skills & Leadership

• Strategic thinking with ability to balance speed and scalability in AI product delivery.
• Strong collaboration, leadership, and communication skills.
• Experience working with distributed and cross-functional teams.
• Ability to evaluate trade-offs between technical and business decisions.

Competency Identifiers:

Technical Expertise in AI and Data Engineering

• Strong background in designing and implementing AI systems,data pipelines, and architectures
• Proficiency in languages like Python, Java, and relevant frameworks.

System Design and Architecture

• Ability to design scalable, secure, and efficient systems that integrate AI and data engineering components
• Prefecient on microservices architecture and cloud computing.
• Proficient with Cloud and Big data tools such as - S3, BigQuery, Snowflake, Redshift, or Databricks

Data Science and Machine Learning

• Strong understanding of data science principles
• machine learning algorithms
• statistical modeling, with experience in applying these concepts to real-world problems.

Leadership & Collaboration

• Proven ability to lead cross-functional teams, collaborate with stakeholders, and communicate technical concepts to non-technical audiences.
• Willingness to stay up-to-date with emerging technologies and trends in AI, data engineering, and cloud computing, and apply this knowledge to drive innovation and improvement.

Delivery & Execution Excellence

• Owns end-to-end technical delivery and system design
• Enforces architecture governance and documentation standards
• Implements scalable CI/CD, DevSecOps, and observability practices
• Proactive in managing technical debt and scalability challenges

Key Performance Indicators (KPIs):

1. System Scalability and Performance: Measure the system's ability to handle increased traffic, data volume, and user adoption, with metrics such as latency, throughput, and error rates.

2. AI Model Accuracy and Reliability*: Track the accuracy, precision, and recall of AI models, as well as their reliability and consistency in production environments.

3. Data Quality and Integrity*: Monitor data quality, integrity, and consistency, with metrics such as data completeness, accuracy, and freshness.

4. Time-to-Market and Deployment Frequency*: Measure the time it takes to develop, test, and deploy new AI features and models, with a focus on reducing cycle time and increasing deployment frequency.

5. Business Value and ROI*: Track the business value generated by AI-powered applications and features, with metrics such as revenue growth, cost savings, and customer satisfaction.

Job Type: Full-time

Pay: Rs500,000.00 - Rs700,000.00 per month

Work Location: Remote