Full-Time
Senior AI and ML Engineer
Posted on 12/5/2025
NVIDIA
10,001+ employees
Designs GPUs and AI HPC platforms
No salary listed
Pune, Maharashtra, India
In Person
 AI & Machine Learning (2) |
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- BE (MS preferred) or equivalent experience in EE/CS with 5+ years of work experience
- Well versed with Large Language Model (LLM), Machine Learning (ML), Agentic AI techniques
- Hands-on experience in leveraging large language models (LLMs) and implementing agentic AI for various workflows
- Expert programming ability in Python, Go or Java
- Experience in working with SQL/NoSQL database systems such as MySQL, MongoDB or Elasticsearch
- Good understanding of distributed systems, understanding of microservice architecture and REST APIs
- Excellent knowledge and working experience with Docker containers. Consistent track record in developing large scale distributed applications
- Ability to effectively work across organizational boundaries to enhance alignment and productivity between teams
- Responsible for building industry insights into AI tools, collaborating with vendors, and evaluating AI based product features
- To accelerate the evaluation of AI tools by developing robust testing environments, with a strong focus on functionality requirement coverage and efficiency
- Responsible for implementing AI/ML use cases to improve efficiency in software development, DevOps and SRE workflows
- Work with software architects and developers in internal and external development teams
- Proactively track AI tool and technology trends, build insights, and collaborate with vendors early to address feature gaps before NVIDIA adoption
- Open to take on SRE or program management responsibilities if need arises
- Expertise in leveraging large language models (LLMs) and Agentic AI to automate complex workflows, with knowledge of retrieval-augmented generation(RAG) and fine-tuning LLMs on enterprise data
- Prior development of a large software project using service oriented architecture operating with real time constraints
NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.
Company Size
10,001+
Company Stage
IPO
Headquarters
Santa Clara, California
Founded
1993
Simplify's Take
What believers are saying
- Data centers generate 89% of $215.9B FY2026 revenue.
- $40B acquisitions like OpenAI bolster AI infrastructure dominance.
- Nemotron models and Drive Thor accelerate agentic AI adoption.
What critics are saying
- AMD MI450X outperforms Blackwell by 25% per watt in inference.
- Huawei Ascend 910D blocks $10B China AI sales due to bans.
- Google TPU v6 cuts hyperscaler GPU dependency by 60%.
What makes NVIDIA unique
- NVIDIA invented GPU in 1999, pioneering accelerated computing.
- CUDA platform from 2006 enables GPUs for AI and HPC.
- Holds 92% discrete GPU market share as of Q1 2025.
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6 month growth
↓ -1%1 year growth
↓ -3%2 year growth
↓ -2%Matlantis has integrated NVIDIA's ALCHEMI Toolkit into its materials simulation platform to accelerate industrial materials discovery. The company previously incorporated NVIDIA Warp-optimised kernels, achieving up to 10x speed improvements in atomistic calculations. The integration includes LightPFP, Matlantis' lightweight potential for large-scale simulations, which uses a server-based architecture with NVIDIA ALCHEMI Toolkit-Ops to reduce communication bottlenecks. Matlantis plans to integrate its flagship Universal Machine-Learning Interatomic Potential with the toolkit to further enhance GPU efficiency. Launched in 2021, Matlantis is a cloud-based atomistic simulator jointly developed by PFN and ENEOS. The platform uses deep learning to increase simulation speeds by tens of thousands of times and serves over 150 companies discovering materials including catalysts, batteries and semiconductors.
Nvidia shares have climbed 18% over a ten-day winning streak, the longest since 2023. The stock is trading about 8% below its October all-time high of $212.19. CEO Jensen Huang revealed at last month's GTC conference that Nvidia has over $1 trillion in GPU orders through 2027, including Blackwell and next-generation Vera Rubin chips. Data centre revenue surged 75% year-over-year and now comprises 88% of the business, a dramatic shift from five years ago when gaming dominated. The rally follows major deals including Meta's February commitment to deploy millions of Nvidia chips across its global data centres. On Monday, Nvidia denied rumours it was pursuing acquisitions of PC makers Dell or HP. The company also unveiled Ising, a new family of open-source models for quantum computing.
D-Wave Quantum CEO Alan Baratz claims quantum computing poses a threat to Nvidia, citing superior energy efficiency. Speaking at the Semafor World Economy Summit, Baratz said D-Wave's quantum computer uses just 10 kilowatts of power—equivalent to five or 10 GPUs—whilst solving problems that would take GPU systems nearly a million years. D-Wave shares rose nearly 16% on Tuesday, part of a 140% gain over the past year. The company reported $2.75 million in Q4 revenue, missing analyst estimates, but bookings surged 471% to $13.4 million. The $5.3 billion company recently secured a $20 million agreement with Florida Atlantic University and acquired Quantum Circuits for $550 million. However, quantum machines remain specialised tools, unable to run large language models that drive Nvidia's dominance.
Vertiv Holdings has been reaffirmed with a Buy rating by Evercore ISI, setting a price target of $280, whilst Barclays raised its target from $281 to $300 with an Overweight rating. The electrical equipment company is partnering with Nvidia on AI infrastructure development. On 16th March, Nvidia introduced its Vera Rubin DSX AI Factory reference design, with Vertiv providing critical power and cooling solutions for AI data centres. The partnership integrates Vertiv's infrastructure expertise with Nvidia's AI systems to enhance energy efficiency and performance. Vertiv is developing Vertiv OneCore Rubin DSX, a prefabricated system designed to accelerate AI factory deployment. The Brussels-headquartered company specialises in critical digital infrastructure technologies for data centres and communication networks.
Nvidia and Dell Technologies are positioned as attractive AI infrastructure investments ahead of their May earnings reports, according to recent analysis. Both companies supply critical hardware for AI computing, with demand for AI capacity continuing to outpace available resources across major cloud services. Nvidia shares have remained flat for six months despite strong fundamentals. Last quarter, its data centre business generated $62 billion in revenue, up 75% year over year, with a 75% gross margin. The company expects over $1 trillion in cumulative orders for its Blackwell and upcoming Rubin chips through 2027. Trading at 17 times next year's expected earnings, Nvidia's valuation appears discounted relative to its 66% revenue growth in fiscal year 2026. Dell Technologies similarly stands to benefit from the AI infrastructure build-out. Both companies report earnings in May.