Ai Engineer

Category: IT/Telecommunication

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Job Description

AI Engineer

We are seeking a talented AI Engineer to design, develop, and deploy cutting‑edge artificial intelligence solutions that drive business value. The ideal candidate will have a strong foundation in machine learning, deep learning, and software engineering, and will thrive in a collaborative, fast‑paced environment.

Key Responsibilities

Design, implement, and optimize machine learning models for a variety of applications (e.g., computer vision, NLP, recommendation systems).
Develop scalable data pipelines and preprocessing workflows to support model training and inference.
Collaborate with product, data, and engineering teams to translate business requirements into AI solutions.
Perform model evaluation, validation, and continuous monitoring in production environments.
Research state‑of‑the‑art algorithms and integrate them into existing systems where appropriate.
Write clean, maintainable, and well‑documented code following best practices and coding standards.
Participate in code reviews, design discussions, and knowledge‑sharing sessions.

Required Qualifications

Bachelor’s or higher degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
3+ years of professional experience building and deploying machine learning models.
Proficiency in Python and deep‑learning frameworks such as TensorFlow, PyTorch, or JAX.
Strong understanding of algorithms, data structures, and software engineering principles.
Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
Familiarity with version control (Git) and CI/CD pipelines.
Excellent problem‑solving skills and the ability to work independently and in a team.

Preferred Skills

Experience with large‑scale data processing tools (Spark, Hadoop, Beam).
Knowledge of MLOps practices and tools (MLflow, Kubeflow, TensorBoard).
Background in statistical modeling, Bayesian methods, or reinforcement learning.
Publications or contributions to open‑source AI projects.
Strong communication skills for presenting technical concepts to non‑technical stakeholders.

Benefits

Competitive salary and performance‑based bonuses.
Comprehensive health, dental, and vision insurance.
Flexible remote‑work policy and generous PTO.
Professional development budget and conference sponsorships.
Access to cutting‑edge hardware and research resources.

To apply, please submit your resume, a cover letter highlighting relevant projects, and any links to portfolios or GitHub repositories.

Job Responsibilities

Design, develop, and implement machine learning and deep learning models to solve business problems.
Build and maintain data pipelines for collecting, cleaning, and preprocessing large-scale datasets.
Conduct research on state‑of‑the‑art AI techniques and evaluate their applicability to project requirements.
Collaborate with product managers, software engineers, and domain experts to define AI‑driven solutions.
Deploy AI models into production environments using appropriate frameworks and cloud services.
Monitor model performance, troubleshoot issues, and continuously improve accuracy and efficiency.
Optimize algorithms for scalability, latency, and resource utilization on target hardware.
Document model architecture, training procedures, and evaluation metrics for reproducibility.
Ensure compliance with data privacy, security, and ethical AI guidelines.
Mentor junior engineers and contribute to knowledge‑sharing initiatives within the team.

Educational Qualification

Master of Science (MSc) , Ai

Preferred Educational Institution

BRAC University

Other Educational Qualification

Certificate in Machine Learning (e.g., Coursera, edX, Udacity)
Professional certification in Deep Learning (e.g., DeepLearning.AI, NVIDIA Deep Learning Institute)
Data Science bootcamps or intensive training programs
Specialized MOOCs in Natural Language Processing, Computer Vision, or Reinforcement Learning
Industry‑recognized certifications such as AWS Certified Machine Learning – Specialty, Google Cloud Professional Machine Learning Engineer, or Microsoft Azure AI Engineer Associate
Research publications, conference papers, or contributions to open‑source AI projects
Workshops, seminars, or short courses on AI ethics, responsible AI, and bias mitigation
Professional memberships in societies like IEEE Computational Intelligence Society or ACM SIGAI

Preferred Industry Experience

IT Industries

Skills

Programming Languages: Python (TensorFlow, PyTorch, scikit‑learn), C++, Java, R, SQL
Machine Learning & Deep Learning: Supervised/unsupervised learning, reinforcement learning, neural network architectures (CNN, RNN, Transformer, GAN)
Data Engineering: Data collection, cleaning, preprocessing, feature engineering, handling big data (Spark, Hadoop)
Statistical & Mathematical Foundations: Linear algebra, calculus, probability, statistics, optimization techniques
Model Evaluation & Validation: Cross‑validation, A/B testing, performance metrics (accuracy, precision, recall, F1‑score, ROC‑AUC)
Model Deployment & Productionization: RESTful APIs, Docker, Kubernetes, serverless (AWS Lambda, Azure Functions), edge deployment
Cloud Platforms & Services: AWS (SageMaker, EC2, S3), Google Cloud (AI Platform, BigQuery), Azure (ML Studio, Blob Storage)
MLOps & CI/CD: Git, GitHub Actions, Jenkins, MLflow, DVC, monitoring, automated testing, model versioning
Natural Language Processing (NLP): Text preprocessing, embeddings (Word2Vec, BERT, GPT), sentiment analysis, language generation
Computer Vision: Image preprocessing, object detection, segmentation, transfer learning, OpenCV, YOLO, EfficientNet
Software Engineering Practices: Clean code, design patterns, unit/integration testing, documentation, Agile/Scrum
Collaboration & Communication: Cross‑functional teamwork, technical writing, presenting findings to stakeholders
Security & Ethics: Data privacy, bias mitigation, model interpretability, compliance (GDPR, HIPAA)

Additional Requirements

Advanced degree (M.Sc. or Ph.D.) in Computer Science, Machine Learning, or a related field.
5+ years of professional experience developing and deploying AI/ML solutions in production.
Deep expertise in Python and at least one major deep‑learning framework (TensorFlow, PyTorch, JAX).
Strong understanding of modern neural network architectures (CNNs, RNNs, Transformers, GNNs) and their trade‑offs.
Experience with large‑scale data pipelines, distributed training, and cloud platforms (AWS, GCP, Azure).
Proficiency in model optimization techniques such as quantization, pruning, and knowledge distillation.
Solid software engineering practices: version control (Git), CI/CD, unit testing, and code reviews.
Ability to translate business problems into scalable AI solutions and communicate results to non‑technical stakeholders.
Familiarity with MLOps tools (Kubeflow, MLflow, Docker, Kubernetes) for model lifecycle management.
Strong analytical mindset with a track record of publishing or presenting research findings.
Excellent problem‑solving skills, attention to detail, and ability to work independently as well as in cross‑functional teams.

Salary

Negotiable

Additional Salary Info

Base Salary: $120,000 – $180,000 per year

Annual Bonus: Up to 20% of base salary, performance‑based

Equity Compensation: Stock options or RSUs valued at $30,000 – $70,000 annually

Signing Bonus: $10,000 – $25,000 (one‑time)

Benefits: Health, dental, vision insurance; 401(k) match; unlimited PTO; remote‑work stipend; professional development budget

Relocation Assistance: Up to $5,000 (if applicable)

Age

25 - 35 Years

Join

Immediately

Other Benefits

Performance bonus
Weekly 2 holidays
Lunch Facilities: Partially Subsidize
Salary Review: Yearly
Festival Bonus: 2

Posted By

ABC ltd.

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Special Instruction

Showcase Relevant Experience: Highlight AI/ML projects, research papers, open‑source contributions, and any production‑grade models you’ve built or deployed.
Quantify Your Impact: Include metrics such as accuracy improvements, speedups, cost reductions, or user adoption numbers to demonstrate real‑world results.
Tailor Your Resume: Use keywords from the job posting (e.g., “deep learning,” “computer vision,” “NLP,” “TensorFlow,” “PyTorch”) and align your skill sections accordingly.
Provide a Portfolio: Link to a personal website, GitHub, or Kaggle profile that contains code samples, notebooks, and detailed project explanations.
Demonstrate Problem‑Solving Ability: Describe the problem, your approach, the algorithms used, and the outcome for each project.
Stay Current: Mention recent courses, certifications, or conferences attended (e.g., Coursera Deep Learning Specialization, NeurIPS, ICML).
Prepare for Technical Interviews:
- Review core concepts: linear algebra, probability, optimization, and model evaluation.
- Practice coding problems in Python, focusing on data structures, algorithms, and ML libraries.
- Be ready to discuss trade‑offs between model complexity, interpretability, and deployment constraints.
Showcase Collaboration Skills: Highlight experiences working with cross‑functional teams (data engineers, product managers, designers) and your ability to communicate technical ideas to non‑technical stakeholders.
Emphasize Deployment Knowledge: Mention experience with containerization (Docker), orchestration (Kubernetes), cloud services (AWS SageMaker, GCP AI Platform, Azure ML), and CI/CD pipelines.
Prepare Thoughtful Questions: Ask about the team’s current AI challenges, data pipelines, model lifecycle management, and opportunities for research or innovation.