AI model engineers
Engineers who train, fine-tune and serve AI models, increasingly large language models, and make them run fast in production, and where in India they are hired.
What this role isThe skills it needs
The skills at a glance
Python
PyTorch
TensorFlow
Transformers
reinforcement learning
Job posts for this role ask most for Python, with PyTorch and TensorFlow to train models, Transformers and reinforcement learning for language models, and SageMaker or Azure OpenAI to run them in the cloud. The full story of what each skill is used for is on the skills page.
Where AI model engineers are hired
An AI model engineer works on the models themselves, training them, adapting them to a company's data and serving them at speed. GenAI engineers build products on top of models without training them, and machine learning engineers build classic prediction models for one task, such as forecasting or recommendations. This is one of the smallest roles in the market, so the work sits in a few places. Big tech is its strongest hiring hotspot, and Google above all. Among product companies, it is hired by the chip makers, the companies that build tools for models, and a few companies whose product depends on a model. The GCCs of insurers, healthcare companies and banks adapt models for their own use. The services firms and the remote hiring platforms take on model work for clients.
Big tech
Big tech is where model work is done at a scale nobody else in India works at, and it hires AI model engineers more readily than any other kind of employer. Google, with Google DeepMind, hires them more heavily than Amazon or Microsoft do, for work close to research. It is also one of the most open places in the market for a junior model engineer (who they hire). Amazon hires the most, for production model work across its retail site, Alexa and AWS (who they hire). Microsoft hires them for the models behind Azure AI and Copilot, and it takes juniors readily too (who they hire). Apple has only a few openings, but model work is a large part of what it hires for in India.
Product companies
Among product companies, model work is hired where the model is the product or where the hardware it runs on is made. Most of these are B2B companies.
The chip makers are a real hiring hotspot. NVIDIA, Qualcomm, AMD and Intel, among processor and GPU makers, need engineers who make models train and run fast on their chips, by tuning, compressing and porting them (who they hire). This is the best home for a model engineer who enjoys performance work. Anyscale, Lexsi Labs and G42 build MLOps and LLMOps platforms, the tools other teams use to train, serve and test models, and this is the role in its purest form. Databricks builds model training into its databases and data warehouses, the platform many companies use to fine-tune their own models.
Some companies hire model engineers because a model sits at the heart of their product. Online marketplaces, like eBay, Myntra and Wayfair, train language models for search, listings and shopping assistants. Thomson Reuters trains and adapts models for legal, tax and news research, part of news and professional information providers. Eightfold and Weekday build AI recruiting and talent intelligence platforms, models that read CVs and match people to jobs. Telnyx builds voice models into CPaaS and business messaging.
A few more pockets are worth knowing. Roku and JioStar adapt models for their streaming services, the apps people use for OTT and live streaming platforms. ValGenesis adapts language models to the regulated documents that go with pharma manufacturing and quality software. Calix, Nokia and Ericsson, in telecom equipment and operators, hire model engineers too. The finance companies, developer tools and education software hire very few.
GCCs
Among the GCCs, model engineers adapt language models to the parent's own documents and data. Most of these roles sit at the centres that run the parent's wider engineering.
The insurers are the GCC hiring hotspot. Inside the insurers, The Hartford, MetLife, Crum & Forster and FM Global adapt language models for claims, underwriting and policy documents. Inside pharma, medical and healthcare companies, Carelon, Optum, Bristol Myers Squibb and Amgen adapt models to clinical and claims data (who they hire). The banks hire the most model engineers of any GCC group, though it is a small part of their hiring. Inside the banks and financial institutions, NatWest Group, Standard Chartered, Wells Fargo and JPMorganChase lead (who they hire). Walmart and Lowe's hire a few for their retail platforms. More on model work across the GCCs is in the GCCs' other roles.
Services firms
In the services world, model engineers adapt models for client projects. The quiet foreign majors, like NTT DATA, CGI, Fujitsu and DXC Technology, are the services hiring hotspot, and almost all of their model work is with language models. The IT services giants, like IBM, Infosys, TCS and Capgemini, hire many model engineers, mostly for machine learning in client projects. The consulting firms' engineering arms, like PwC India, Deloitte and EY, hire a few and take juniors more readily (who they hire). Accenture has openings too, though model work is a tiny part of its hiring, so competition for them is wide (who they hire). More on all of them is in the services world's smaller roles.
Staffing firms and hiring platforms
The hiring platforms, like Turing, Uplers, hackajob and Mindrift, are one of the largest sources of model work in India. Much of it is remote work for AI companies abroad, including training and testing their models (who they hire). The staffing companies rarely place this role.
Where AI model engineers are rarely hired
AI model engineers are rarely hired by the finance product companies, the developer-tool and cloud companies, or education and government software. The GCCs of manufacturers, energy majors and telecom operators hardly hire any, and neither do the mid-sized services firms or the engineering-services firms.
Roles next door
GenAI engineers build products on top of the models that model engineers train.
Machine learning engineers build classic prediction models for one task.
Python backend engineers build the services that serve a model to its users.
Data engineers prepare the data that models are trained on.
Terms used on this page
Fine-tuning Training an existing model further on a company's own data so it does a specific job better.
Serving Running a trained model in production so that apps can call it quickly and reliably.
Large language model A model such as GPT or Claude, trained on huge amounts of text.
B2B Business to business. A company that sells software or services to other companies.
GCC Global capability centre. A global company's own engineering centre in India, building software for its parent.
Last updated October 2026.