Jun 4, 2025
8 Min
 min read

Building a Clear, Delivery-Driven Career Framework for AI Engineers

Why We Needed Structure

At Ontik Technology, as we began expanding our AI practice, we encountered a familiar challenge:

How do we define what an AI Engineer is not just theoretically, but in terms of day-to-day responsibilities, real-world delivery, and career development?

Across many organizations, AI roles are often shaped by immediate project needs or individual strengths. While this flexibility has merit, it lacks consistency. Without a clear career path for AI engineers, it becomes difficult to guide learning, evaluate maturity, or prepare engineers for production-ready AI systems.

We needed a structure that reflects how AI projects are delivered, especially within startup environments:

  • Not just building AI models, but deploying and integrating them.
  • Not just writing code, but developing and maintaining pipelines.
  • Not just one-off results, but ensuring reproducibility, scale, and continuous feedback.

So, we built a grounded framework and we’re sharing it to invite collaboration, offer structure to others building AI solutions, and foster alignment across the broader ecosystem of AI engineer careers.

Emerging AI-Native Career Fields: 2025–2030

Artificial Intelligence (AI) has quickly moved from being a futuristic idea to a powerful tool in today’s industries. It helps businesses work faster, make better decisions, and is changing how people build their careers. As AI continues to evolve, it is creating new jobs and altering the skills required for traditional roles.

AI is utilized in various fields, including healthcare, finance, education, and manufacturing. More companies are adopting technologies like machine learning, robotics, and natural language processing. This has increased the demand for people who understand these tools. As a result, AI careers are growing fast and offer high salaries for those with the right skills.

In South Asian countries like Bangladesh, India, and China, AI job opportunities are rising quickly. These countries are becoming major tech hubs. Jobs such as machine learning engineers, AI specialists, and data scientists are now among the most in-demand and best-paying roles. In South Asia, especially, the future of AI looks bright, with government support and company investments helping AI grow across many industries. This is creating exciting career opportunities for many people.

While much of the public focus remains on job losses due to AI, the technology is also fueling the rise of entirely new professions. By 2030, employment in STEM-related fields is expected to grow by 23%, with roles in AI and machine learning leading the way, projected to increase by 40% by 2027.

Some of the most promising emerging roles include:

  • Data analysts and digital transformation experts, with an anticipated growth of 30–35%
  • Healthcare professionals and technicians, contributing to a combined 5.5 million new jobs
    AI trainers, prompt engineers, and quality assurance specialists in AI systems

Our View: AI Engineering = Data + Modeling + Production Integration

To us, an AI engineer is a professional who operates across:

  • Data Engineering – Cleaning, transforming, and understanding large-scale data.
  • Model Building – Developing and fine-tuning machine learning models (from classical ML to deep learning).
  • System Integration – Connecting models to the broader ecosystem via APIs, lifecycle tools, and monitoring.

We distinguish this from simply using LLM APIs or generative AI prompt engineering. While valuable, that falls under solution development not core AI systems engineering.

True AI Engineers do more than experiment , they build AI systems with intentional alignment to business goals, delivery timelines, and technical best practices.

The Career Ladder for AI Engineers

This AI Engineer Career framework outlines growth from beginner to strategic leadership across six levels. It helps run AI careers with clarity, supports mentorship, and aligns expectations across teams. Let’s walk through the ladder at a high level without getting into the details.

1. AI Intern

Focus: Learn structured experimentation, data cleaning, and basic scripting

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

Goal: Contribute to early-stage AI development, especially in preprocessing and exploratory modeling.

Ideal for entry-level AI engineer roles or those seeking immersion in applied AI frameworks.

2. Junior ML Engineer / AI Analyst

Focus: Execute structured experiments and track results

  • Feature engineering, EDA, encoding
  • Baseline validation (AUC, accuracy, F1)
  • Tools like MLflow and n8n

Goal: Support machine learning reproducibility and model evaluation efforts.

A natural next step for learning experts seeking to effectively utilize AI tools.

3. Career in AI: Associate AI Engineer

Focus: Own small model pipelines and get models integration-ready

  • Classical ML (XGBoost, SVMs), basic CNNs
  • Identify data leakage, alignment issues
  • Deploy with FastAPI, Docker
  • Track with DVC, MLflow

Goal: Deliver reliable, testable, and integration-ready AI applications.

This level reflects the transition from experimentation to real-world AI systems engineering.

4. AI Engineer

Focus: End-to-end model ownership and delivery

  • Fine-tune pretrained models (BERT, GPT)
  • Handle imbalance, time-series modeling, focal loss
  • CI/CD deployment, SHAP, drift monitoring
  • Real-time and batch integration with scalable tooling

Goal: Deliver robust, production grade AI features confidently.

The core of any AI Engineer Career, where AI engineers work at the heart of delivery, infrastructure, and business impact.

5. Senior AI Engineer

Focus: System level design, mentoring, and optimization

  • Modular pipelines, data versioning
  • Tuning (Optuna), fairness audits, adversarial testing
  • Workflow orchestration (Airflow, n8n)
  • Mentor junior engineers and ensure team reproducibility

Goal: Scale AI capabilities and foster team growth.

Ideal for an engineering manager AI integration future skills role or senior contributor guiding AI initiatives.

6. Lead AI Engineer

Focus: Technical leadership and strategic oversight

  • Evaluate architecture and system design trade-offs
  • Align product roadmaps with AI infrastructure
  • Collaborate with DevOps, infra, backend
  • Lead platform-level innovation and proof-of-concepts

Goal: Guide organizational AI engineering direction and champion AI skills development.

A top-tier role for those aspiring to become an artificial intelligence expert or lead applied AI engineering teams.

Why This Framework Matters

At Ontik, this AI Engineer Career path helps us:

  • Make AI jobs measurable and transparent — whether AI engineer jobs are remote or hybrid
  • Guide upskilling, from entry level AI engineer to expert engineer
  • Match responsibility with technical maturity
  • Differentiate between AI solutions engineer roles and software engineer support in AI systems

We’re not just hiring for titles — we’re developing AI and engineering capabilities for long-term impact.

Why We’re Sharing This

In Bangladesh and beyond, the demand for AI engineers is rapidly growing. We believe the AI ecosystem will thrive when we:

  • Share structured paths for becoming an AI engineer
  • Support roles like AI dev jobs, artificial intelligence engineer jobs remote, or AI jobs for beginners
  • Encourage pathways like how to build your career in AI or transition from software engineering to machine learning
    Help answer critical questions:

    • Can I learn AI and ML on my own?
    • How to become an AI engineer without a degree?
    • What math do I need to know for AI?
    • Is engineering a good career?
    • Which companies hire AI engineers?

We’re here to connect, collaborate, and co-create best practices.

Let’s Build the AI Ecosystem — Together

At Ontik Technology, we’ve developed this AI engineering best practices framework to reflect real-world AI system delivery — from experiment to execution.

We invite engineers, mentors, and founders to join us in:

  • Creating shared expectations for a career in AI
  • Bringing clarity to AI req and project delivery
  • Promoting accountability, confidence, and growth in AI careers
  • Inspiring future artificial intelligence specialists, AI operators, and top AI experts

Together, let’s grow intelligent engineering in our region. Let’s define what it means to be an AI Engineer — with clarity, confidence, and care. Let’s build, align, and inspire the future of AI and engineering — in Bangladesh and beyond.

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Moshiur Rahman
Chief Operating Officer

Moshiur, a Computer Science and Engineering graduate from BRAC University, has a multifaceted background combining education and entrepreneurship. His journey began as a lecturer in CSE at BRACU, showcasing his commitment to knowledge dissemination. Transitioning into the entrepreneurial sphere, Moshiur co-founded Mindcraft Labs, a prominent IT training institute in Bangladesh, emphasizing his dedication to fostering the country's tech talent. With a fervent interest in emerging technologies and a knack for effective team management, Moshiur currently serves as the Chief Operating Officer at Ontik Technology. In this role, he leverages his technical expertise, educational experience, and leadership skills to drive operational excellence and technological innovation within the company, playing a pivotal role in Ontik's advancement and success.

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