AI & Machine Learning Engineer

Job Summary

AI & ML Engineer

Key Responsibilities

1. AI/ML Model Development

• Design, build, and train machine learning models (supervised, unsupervised, NLP, deep learning).
• Develop predictive models and algorithms to address business problems.
• Optimize models for accuracy, performance, and scalability.

2. Data Engineering & Processing

• Work with large datasets: cleaning, preprocessing, transforming, and feature engineering.
• Collaborate with data engineers to improve data quality and pipeline efficiency.
• Build reliable data ingestion processes.

3. AI System Deployment

• Deploy machine learning models into production environments.
• Build and maintain end-to-end ML pipelines (MLOps).
• Monitor model performance, retrain models, and manage lifecycle updates.

4. Research & Innovation

• Stay updated with the latest trends in AI, deep learning, LLMs, and automation.
• Experiment with new frameworks, architectures, and techniques.
• Conduct PoCs (Proof of Concepts) for emerging AI-driven solutions.

5. Cross-Team Collaboration

• Work with product, tech, and business teams to define AI requirements.
• Communicate findings and insights through reports, dashboards, and visualizations.
• Integrate AI models with existing software applications or platforms.

Required Skills & Qualifications

Technical Skills

• Strong knowledge of ML algorithms, deep learning architectures, and NLP techniques.
• Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, Keras).
• Experience with data tools (Pandas, NumPy, SQL, Spark).
• Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps tools (Docker, Kubernetes, CI/CD).
• Understanding of LLMs, vector databases, embeddings, and generative AI tools (optional but a huge plus).

Education & Experience

• Bachelor’s/Master’s degree in Computer Science, AI, ML, Data Science, or related field.
• 2+ years of experience in machine learning or AI development.
• Prior experience deploying models into production is preferred.

Soft Skills

• Strong problem-solving and analytical thinking.
• Excellent communication and collaboration skills.
• Ability to work in fast-paced, agile environments.
• Curious, innovative, and proactive mindset.

Preferred Qualifications (Optional)

• Experience with Reinforcement Learning, Transformers, or advanced Deep Learning.
• Knowledge of LLM fine-tuning, RAG systems, or AI automation workflows.
• Contributions to open-source ML projects.

Salary

• Competitive and based on experience.