Jun 15, 2025
8 Min
 min read

Building a Practical AI Engineer Career Framework: From ML Model Building to MLOps and Real-World Delivery

Why We Needed a Clear Framework for AI Engineers

At Ontik Technology, as we grew our applied AI and machine learning practice, we faced a challenge many companies know well:

How do we clearly define what an AI Engineer truly is—not just in theory, but in daily work, model development, and production deployment?

Across the industry, AI roles often adapt to specific projects or the unique strengths of individuals. While this flexibility can be helpful, it often results in vague job expectations and unclear career paths. Without a solid framework, it’s tough to support engineers’ growth, evaluate their skills objectively, or prepare them to deliver production-quality AI systems.

We realized we needed a practical, delivery-driven AI engineer career framework—one that reflects the full machine learning lifecycle and real-world AI system delivery, especially in fast-moving startups or product teams.

It’s about:

  • Not just building models, but building and deploying robust ML pipelines that run reliably in production.
  • Not just writing code, but designing scalable, maintainable AI infrastructure.
  • Not just chasing accuracy numbers, but ensuring reproducibility, latency optimization, drift detection, and other key MLOps practices.

Our Perspective: AI Engineering is More Than Just Using Pre-Trained Models

Using GPT APIs or building workflows with pre-trained large language models is valuable work, but in our view, that belongs more to AI solution development.

True AI Engineers go a step deeper.

They understand the fundamentals of building models from scratch. They write modular, clean, and testable code. They think carefully about reproducibility, latency, drift detection, model versioning, and production readiness.

They don’t just plug in pre-trained models—they design and build AI systems end-to-end, often starting from raw data, through model training, tuning, and integration into scalable applications.

Their work brings together multiple key areas:

  • Data engineering, which involves preparing and transforming large datasets to make them usable and reliable for modeling.
  • Model building, where they design, train, and fine-tune machine learning and deep learning models—often building them from scratch to solve specific problems.
  • API development for models, creating interfaces that allow other systems or applications to interact seamlessly with the AI models in production.
  • MLOps and system integration, focused on deploying models into production, setting up monitoring to catch issues like data drift, and maintaining the AI systems to ensure they perform reliably over time.

This comprehensive approach ensures AI solutions aren’t just experiments—they are real, scalable, and maintainable machine learning systems.

The AI Engineer Career Ladder: From Intern to Lead

We designed a career ladder to help AI professionals grow and companies to set clear expectations. This ladder includes six levels, each with distinct roles, responsibilities, and goals.

1. AI Intern

Focus: Learn the basics of data preprocessing, scripting, and simple model building.

Key skills:

  • Python, pandas, NumPy
  • Simple models (like logistic regression)
  • Version control and reproducible notebooks

Goal: Contribute to data cleaning and initial model experiments in a structured, collaborative setting.

2. Junior ML Engineer / AI Analyst

Focus: Run well-defined machine learning experiments and track results carefully.

Key skills:

  • Feature engineering, exploratory data analysis (EDA)
  • Baseline modeling and validation metrics (AUC, accuracy, F1 score)
  • Experiment tracking tools like MLflow or n8n

Goal: Support model evaluation and reproducibility for early-stage ML projects.

3. Associate AI Engineer

Focus: Own small ML pipelines and prepare models for system integration.

Key skills:

  • Classical ML algorithms (XGBoost, SVM), small CNNs/RNNs
  • Identify data leakage and alignment problems
  • Package models with FastAPI and Docker
  • Use tools like DVC and MLflow for pipeline modularity

Goal: Deliver reliable, testable ML components ready for production integration.

4. AI Engineer

Focus: Take end-to-end ownership of production-ready machine learning features.

Key skills:

  • Fine-tune pretrained models (BERT, GPT) and build from scratch
  • Handle imbalanced datasets, time-series splits, and advanced loss functions
  • Deploy via CI/CD pipelines and monitor with tools like SHAP or drift detection
  • Integrate models into batch or real-time production systems

Goal: Deliver scalable AI solutions aligned with MLOps best practices and infrastructure requirements.

5. Senior AI Engineer

Focus: Architect scalable ML pipelines and mentor junior engineers.

Key skills:

  • Design modular, versioned pipelines
  • Advanced hyperparameter tuning (Optuna), fairness audits
  • Robustness testing and orchestration using Airflow or n8n
  • Lead reproducibility and performance improvements across projects

Goal: Drive technical standards for scalable ML systems and guide team growth.

6. Lead AI Engineer

Focus: Provide technical leadership, align strategy, and oversee AI delivery.

Key skills:

  • Evaluate architecture trade-offs across multiple projects
  • Align timelines and scope with product, backend, and DevOps teams
  • Collaborate on secure, automated ML deployment pipelines
  • Lead POCs and platform-level AI innovation efforts

Goal: Set vision and execution strategy for AI delivery across teams and projects.

Why This Framework Matters

At Ontik Technology, having a clear, transparent AI engineer framework helps us:

  • Define roles and responsibilities clearly, making career paths measurable and fair.
  • Guide structured mentorship and learning opportunities across the team.
  • Align technical maturity with real project delivery needs.
  • Separate AI solution development (like prompt engineering with LLMs) from core model engineering and MLOps.
  • Build scalable ML pipelines and maintainable AI infrastructure grounded in real-world best practices.

Let’s Build the AI Engineering Ecosystem — Together

This framework reflects how AI and machine learning systems are truly built and delivered—from data collection and model building to deployment and ongoing monitoring. It’s not a rigid rulebook, but a practical starting point designed to spark conversation, alignment, and shared learning.

Our goal is to build a community that:

  • Defines what it means to be a true AI Engineer—with clarity, structure, and a strong focus on delivery.
  • Nurtures the next generation of machine learning talent.
  • Builds sustainable, robust AI ecosystems in Bangladesh and beyond.

If you’re involved in machine learning engineering, AI model development, building MLOps pipelines, or leading AI teams, this framework offers practical guidance to:

  • Clarify roles and expectations,
  • Improve collaboration between data scientists, engineers, and DevOps,
  • Deliver reliable, scalable AI applications that truly add value.

We see this as a living framework—one that can be continually improved with input from engineers, leaders, and teams working on real-world AI projects. As the AI ecosystem in Bangladesh and beyond grows rapidly, creating shared language and clear expectations will help all of us raise the bar for AI engineering.

We welcome your feedback, suggestions, and collaboration to refine this framework further. Together, we can shape a stronger, more structured, and delivery-focused AI engineering culture—building scalable, robust AI systems that make a real impact.

#AIEngineering #CareerFramework #MLOps #BangladeshTech #OntikAI

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Sarwar Hossain
Technical Lead

Sarwar Hossain is a seasoned engineering professional with over 7 years of experience in backend systems, DevOps, and full-stack development. As Technical Lead at Ontik Technology, he plays a critical role in leading engineering execution and platform architecture across key products, including AI-powered solutions. With a focus on scalable system design and delivery excellence, Sarwar oversees the full development lifecycle — from technical discovery and architecture to deployment, performance tuning, and developer experience. His prior roles at Genweb2, NEXT Ventures, Enosis Solutions, and Divine IT Limited reflect deep hands-on expertise across ERP, fintech, and cloud-native platforms.

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