A career in Machine Learning and Generative AI engineering offers strong long-term growth, high technical relevance, and exposure to cutting-edge technologies. The ML / GenAI Application Engineer role is designed for professionals who want to build real-world AI-powered applications and work at the intersection of software engineering, data science, and artificial intelligence.
This opportunity is ideal for candidates passionate about applying machine learning and generative AI to solve complex business problems at enterprise scale.
Company Snapshot
is a trusted global organization providing news, data, and technology solutions to professionals across legal, tax, accounting, finance, and risk domains. The company is heavily investing in artificial intelligence and automation to enhance its platforms and products.
Working in such an environment offers exposure to large-scale data systems, ethical AI development, and mission-critical enterprise applications, making it an excellent place to grow as an AI engineer.
Role Overview – ML / GenAI Application Engineer
The ML / GenAI Application Engineer role focuses on designing, developing, and deploying intelligent applications powered by machine learning and generative AI technologies. Engineers in this role work across the full lifecycle of AI systems, from experimentation to production deployment.
Key role details:
- Position: ML / GenAI Application Engineer
- Employment Type: Full Time
- Location: India
- Domain: Machine Learning / Generative AI / Software Engineering
This role combines hands-on coding with applied AI problem-solving in production environments.
Role Purpose and Business Impact
The primary purpose of this role is to translate advanced AI models into scalable, reliable applications that deliver real business value. Engineers help automate workflows, enhance information retrieval, improve content understanding, and support decision-making through intelligent systems.
Your work directly impacts:
- Product intelligence and automation
- User experience and productivity
- Accuracy and efficiency of AI-driven solutions
- Responsible and ethical use of AI technologies
Key Responsibilities
- Design and develop ML and generative AI-powered applications
- Work with structured and unstructured datasets
- Train, fine-tune, and evaluate machine learning models
- Integrate AI components into production software systems
- Optimize model performance for scalability, latency, and cost
- Collaborate with software engineers, data scientists, and product teams
- Monitor deployed models and improve performance over time
- Ensure robustness, reliability, and responsible AI usage
- Document system design, experiments, and best practices
These responsibilities help engineers build both technical depth and system-level thinking.
Skills Required
Candidates applying for this role should have a strong foundation in both AI and software engineering.
Core technical skills include:
- Strong programming skills in Python or similar languages
- Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn
- Understanding of generative AI concepts, including transformers, embeddings, and large language models
- Experience with data preprocessing, model training, and evaluation
- Familiarity with ML deployment and model serving approaches
- Knowledge of APIs and backend integration
- Understanding of performance optimization and scalability
- Exposure to cloud platforms and MLOps practices is an advantage
Who Can Apply
This role is suitable for:
- Engineers with experience or strong interest in ML and AI systems
- Professionals transitioning from software engineering to applied AI
- Candidates with hands-on AI projects, research, or internships
- Individuals passionate about building production-ready AI solutions
Strong fundamentals, curiosity, and problem-solving ability matter more than specific titles.
Work Environment and Learning Culture
The work culture emphasizes innovation, collaboration, and continuous learning. Engineers work in cross-functional teams, participate in technical discussions, and contribute to decisions around AI design and implementation.
Learning is supported through:
- Exposure to real enterprise AI use cases
- Collaboration with experienced AI and engineering professionals
- Experimentation with modern AI techniques
- Continuous improvement through feedback and iteration
This environment is ideal for professionals who want to stay at the forefront of AI engineering.
Career Growth Opportunities
Starting as an ML / GenAI Application Engineer can lead to roles such as:
- Senior Machine Learning Engineer
- Generative AI Specialist
- AI Solutions Architect
- Data Science Lead
- AI Platform Engineer
- Engineering Manager (AI/ML teams)
AI engineering skills remain highly transferable and in demand across industries.
Why This Role Is Career-Strong
This role stands out because it:
- Works with high-growth AI and GenAI technologies
- Builds production-level ML and systems experience
- Offers long-term relevance and career stability
- Enables impact at enterprise scale
- Supports continuous technical and professional growth
Selection Process
The typical hiring process may include:
- Online application
- Resume shortlisting
- Technical interviews covering ML concepts, coding, and system design
- Problem-solving or case discussions
- Behavioral or team-fit interviews
Clear explanation of AI concepts, strong coding fundamentals, and structured thinking improve selection chances.
