Generative AI & LLM Engineer Careers: Breaking Into Top Tech Roles in 2025

Generative AI is not just the latest buzzword; it’s reshaping the foundation of engineering careers. Gartner predicts that four out of five engineers will need to upskill by 2027 to stay relevant in generative-AI-shaped roles. Enterprises are competing for generative AI and LLM engineers, and India is at the heart of this shift (Source: Gartner). 

Reports indicate that India needs at least 1 million skilled AI professionals by 2026, opening unprecedented opportunities for those who act now (Source: ET). India can greatly benefit from the generative AI boom, given that it addresses the gap relating to the scarcity of AI talent.   

In this article, we look at how you can land generative AI and LLM engineer jobs in the Indian market in 2025. 

What Does a Generative AI Engineer Do?

They design, develop, fine-tune, and deploy generative models, mostly LLMs. The models create new content such as text, images, or code, using techniques like GANs and transformers. 

Responsibilities:

• It involves selecting appropriate generative model architectures, designing components, and developing their algorithms
• With generative models creating huge amounts of data, generative AI engineers must curate, clean, and preprocess the datasets for quality and to reduce biases
• A major part of the role involves training complex models for efficiency and performance
• Evaluate model outputs and identify shortcomings to improve performance
• Generative AI and LLM engineers must stay updated with the latest research and experiment with new techniques

How Generative AI Engineers Differ From a Traditional ML Engineer or Data Scientist:

Generative AI Engineer Skills You Need to Succeed:

To become a successful generative AI and LLM engineer, you must have theoretical knowledge, programming skills, and an understanding of the latest tools, technologies, and platforms. Six in ten leaders expect Gen AI to transform their organizations, underscoring an urgent need for upskilling (Source: WEF). 

 Let’s look at the core skills you need to succeed in generative AI jobs. 

Core Foundation of generative AI jobs: 

Mathematics:

• Linear Algebra, Calculus, Probability, and Statistics: They are foundational in understanding how generative models and neural networks work, especially in optimization, model training, and probabilistic reasoning. 

Machine Learning & Deep Learning Fundamentals:

• Core concepts: Supervised/Unsupervised Learning, Optimization Algorithms, Neural Networks, Backpropagation, Regularization, and Loss Functions. They are essential for model training, evaluation, and improvement. 

Generative Model Architectures

• Transformers & Large Language Models (LLMs)- They are central to current GenAI applications (example: ChatGPT, Bard, Claude)
• Diffusion Models- They are rapidly growing in importance, especially in image, audio, and multi-modal generation
• GANs & VAEs- These are important foundational models, and are valuable for conceptual understanding and specific use cases

Programming Skills

• Python Proficiency- This is the de facto language for AI/ML development
• You need a strong grasp of data structures, algorithms, and object-oriented programming (OOP) for building scalable and efficient codebases

AI/ML Libraries & Frameworks

• PyTorch (preferred for research and production)
• Hugging Face Transformers (for working with LLMs)
• NumPy, Pandas, and Scikit-learn
• TensorFlow/Keras (even though they are less central in GenAI, they are still used in some environments)

Cloud & Deployment Skills

• Hands-on experience with cloud platforms: AWS, Google Cloud Platform (GCP), Microsoft Azure
• Containerization & orchestration tools: Docker and Kubernetes. They are essential for deploying scalable and reproducible GenAI applications

MLOps & LLMOps Tooling: 

Familiarity with the following modern operational tools is highly preferred:

• MLflow, Weights & Biases- For experiment tracking and model management
• LangSmith, Ray – They are emerging tools for LLMOps, orchestration, and serving
  

Complementary Skills for Generative AI Careers:

Apart from technical prowess, there are several complementary skills that make you a better generative AI and LLM engineer. These are the skills that help generative AI engineers apply their technical skills in the real world. 

Below are some of the complementary skills to excel at generative AI jobs:

• Need strong data engineering skills, such as data collection, cleaning, transformation, and pipeline management, to get high-quality and relevant data. It ensures that the models are trained on clean, relevant, and representative datasets
• MLOps understanding is critical since it ensures that the models are reliable, maintainable, and can perform well in real-world conditions
• Your deep understanding of a particular industry or domain is important since it helps identify relevant problems, customize models to the specific needs, and understand the results with context
• When working on generative AI projects, it is almost always collaborative. Since there is close collaboration with data scientists, traditional ML engineers, designers, experts, and product managers, the ability to communicate properly is highly helpful
• You must possess a strong aptitude for problem-solving, develop novel architectures, overcome technical issues, and be open to experimentation
• Engineers must be aware of the potential biases in data and models, implications of synthetic content (like deepfakes), intellectual property rights, and privacy concerns. There should be commitment toward developing and deploying AI responsibly 

Key Tools Every Generative AI and LLM Engineer Should Master:

The generative AI landscape has a suite of tools and platforms that make it happen. Being proficient in these tools is required for generative AI and LLM engineers to build innovative applications. Let’s look at some of the key tools used in generative AI jobs. 

• Model Hubs & Frameworks: Hugging Face Hub, PyTorch Lightning/TensorFlow, Diffusers Library
• Model Serving & Optimization: vLLM for high-throughput inference, token streaming, caching, NVIDIA TensorRT-LLM for GPU-optimized inference for latency/cost efficiency, Ollama for Lightweight local deployment of open-weight LLMs, and Ray for scaling ML/LLM workloads across clusters.
• LLM Application Frameworks: LangChain and LlamaIndex help build complex chains, agents, and Retrieval-Augmented Generation (RAG) applications
• Vector Databases: Pinecone, Weaviate, ChromaDB, and Milvus
• Cloud AI Platforms: AWS Bedrock, Google Vertex AI, Azure OpenAI Service
• MLOps and Deployment: Docker, Kubernetes, MLflow, Kubeflow, and Prometheus/Grafana for monitoring
• Evaluation, Observability & Monitoring:  LangSmith for tracing, debugging, dataset-driven evals for LLM apps, weights & biases (W&B)f for experiment tracking, eval pipelines, system monitoring, Ragas/DeepEval for specialized evaluation for RAG apps (faithfulness, groundedness, answer relevance), OpenAI Evals / HumanEval sets for for benchmarking generative performance
• Fine-tuning & Model Adaptation: PEFT (LoRA, QLoRA) for parameter-efficient fine-tuning for custom LLMs, TRL (Transformers Reinforcement Learning) for RLHF, DPO, PPO for aligning models to preferences, quantization libraries for bitsandbytes, GGUF, GPTQ to run models on constrained hardware
• Specialized Hardware: Understanding of GPU/TPU utilization and optimization

Generative AI Engineer Salary vs. Machine Learning Engineer Salary:

The demand for generative AI engineers has an impact on the salary benchmarks, as generative AI engineers are placed at a premium compared to machine learning engineers. It shows the novelty of the field, specialized skill set, and the value that it brings to businesses. 

Generative AI Courses to Kickstart Your Career:

Learning from courses and workshops is a great way to enter the generative AI field. Several platforms offer comprehensive courses, using which you can build your portfolio to showcase to employers. Below are some of the reputable courses on offer for building a foundation in generative AI.

DeepLearningAI: They offer a range of generative AI courses that include “Generative AI for Everyone” and “Generative AI with LLMs.” These two courses cover the fundamentals of gen AI, practical prompt engineering, and techniques to build gen AI applications (Source: DeepLearningAI). 

Coursera: They also offer a variety of generative AI courses from several universities and companies that cater to various skill levels. The “Introduction to Generative AI” by Google Cloud and the "AWS Generative AI Applications Professional Certificate" are great places to start (Source: Coursera).

AWS and Google Cloud: They offer training and certification programs in generative AI. The programs focus on their respective cloud services and tools for building GenAI tools. (Source: AWS, Google Cloud)

Indian Platforms: GUVI, Analytics India Magazine, and NASSCOM have curated learning tracks that are applicable for entry-level to expert AI skills (Source: GUVI, NASSCOM)

Building a Portfolio:

While theoretical knowledge is a must, you must also demonstrate your practical skills by engaging in projects, hackathons, and contributing to open-source projects.

Projects: Work on projects that show your ability to create generative models. It could be building chatbots or code generators that create image synthesis applications. ProjectPro offers several generative AI project ideas that you can practice (Source: ProjectPro).

GitHub: Having a well-documented GitHub repository that has clear explanations and examples is a great way to show your generative AI skills.

Hugging Face Spaces: Here, you can host and share your machine learning demo apps directly. Using this, create an interactive portfolio that employers can check out (Source: Hugging Face Spaces).

Hackathons: AI and generative AI-focused hackathons are a great way to work on real-world problems, collaborate with like-minded people, and build prototypes rapidly. 

Events like Tredence’s Infinity AI are an excellent arena to network, gain practical experience, and listen to generative AI experts. The event had 1000+ participants, a 24-hour live coding sprint, and top teams got awarded. 

Open-Source Contributions: Participate in open-source generative AI projects to show off your skills and use them as a portfolio too. 

Stay Updated: Leverage platforms like KDnuggets, Towards Data Science, Data Science Central, Analytics India Magazine, and O’Reilly Media. 

Generative AI Opportunities Across Industries:

There is a wealth of generative AI opportunities in every sector, particularly in India. Its applicability is driving demand for gen AI jobs in all stages. 

• Healthcare: AI-powered diagnostic tools, virtual assistants for patient support, systems for drug discovery and personalized medicine (Source: EY)
• Finance: Fraud detection, personalized financial advisory, automated report generation, and risk assessment. Generative AI can increase productivity in the Indian banking industry by 46% by 2030 (Source: FutureSkillsPrime)
• Retail and E-Commerce: Enables hyper-personalization, optimizes supply chains, enhances customer service, does product research, generates marketing content, and so on (Source: DQIndia)
• Manufacturing: Helps with predictive maintenance, quality control, and optimizing design processes, resulting in efficient and responsive systems
• Media: Generates scripts and music, creates realistic visual effects and virtual characters, and synthetic data

Challenges in the Field of Generative AI:

Despite the immense potential and rapid advancements, the generative AI field has its own challenges. Let’s look at some of them.

• Even though India’s cloud infrastructure adoption is growing, its access and scale can be costly (Source: MITNews)
• The pace at which the technology is evolving is making it challenging for engineers and organizations to keep up
• Since gen AI models are trained on large datasets, there are significant privacy concerns 
• The data generated can reflect biases that lead to unfair outcomes
• Gen AI might create content that mimics existing styles, raising complex legal and ethical questions
• Its power to create highly realistic synthetic data (deepfakes) poses a big threat to misinformation and disinformation

These challenges also create several opportunities for skilled generative AI and LLM engineers who can solve them. 

Mindset for Thriving in Generative AI Jobs:

Beyond technical and its complementary skills, there should also be a certain disposition towards learning, problem solving, and ethical considerations. 

Let’s look at the mindset required to be good at generative AI jobs:

1. Curiosity: 

The generative AI space is known for its constant innovations and new discoveries. A deep sense of curiosity is a must to stay ahead in this field

2. Adaptability

There are newer models, frameworks, and best practices that emerge frequently. Therefore, engineers must be willing to quickly adapt to learn new methods and unlearn the old ones. When technology keeps evolving, it helps you pivot with ease

3. Open to Experimentation: 

Gen AI development involves a high level of experimentation. There is no clear path to success and many solutions emerge through trial and error. Being open to experimentation and learning from failures is crucial. 

4. Innovation With Responsibility:

When working toward cutting-edge capabilities, consider the ethical side of it, understand that there will be societal impact, and act based on long-term safety. Have active discussions on AI governance, policy, and the impact on employment.

What Tredence Values in a Generative AI and LLM Engineer:

As one of the leading data science and AI engineering companies, Tredence’s approach to hiring generative AI and LLM engineers is based on the skills and mindset required to succeed in this field. Some of the main values we look for include:

• Deep understanding of Gen AI models, architectures, and the underlying mathematical and computational principles
• The ability to think innovatively to solve complex, real-world business challenges
• A strong commitment to responsible AI practices is pivotal for generative AI jobs
• Being able to transform AI capabilities into business value
• Work effectively with cross-functional teams and communicate easily with non-technical stakeholders 
  

Conclusion:

India’s innovation ecosystem will owe some of its major success to generative AI engineers and LLM specialists. They drive business transformation and competitive advantage across multiple sectors. The opportunity to create a lucrative and impactful career in India’s generative AI space has never been greater. 

Start now, invest in courses, build a portfolio, check out hackathons, and community platforms. There is demand across every sector in India, coupled with competitive salaries and remote working opportunities. 

Are you all set to test your data science skills with real-world projects? Explore the open positions at Tredence and learn how the TAL program accelerates your growth. 

FAQs:

What is the future of generative AI in 2025?

With generative AI making a huge dent in the way businesses across every sector operate, you can safely assume that it is not a passing trend. The demand for generative AI jobs is higher than the available supply, according to reports. 

Which jobs will be replaced by AI in 2025?

Telemarketers, retail cashiers, data entry clerks, customer service representatives (basic support), and paralegals are some of the job roles that might get replaced by AI in 2025.

What is the next technology after generative AI?

Integrative AI systems are said to be the future of AI. It involves systems that are intelligent and generative. They combine data from different sources, offering efficient and effective solutions.

What is the highest package offered in generative AI jobs?

The salaries for generative AI roles in India start from 6 lakhs/annum. 

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Editorial Team
Tredence