Senior AI Models GPU Deployment Software Engineer Job at AMD (Bangalore)

Senior Manager, Performance AI/ML Network Deployment Engineering

The Senior Manager, DC GPU Advanced Forward Deployment and Systems Engineering i...

Location

United States , Santa Clara

Salary:

210400.00 - 315600.00 USD / Year

AMD

Expiration Date

Until further notice

Requirements

Expertise in networking and performance optimization for large-scale AI/ML networks, including network, compute, storage cluster design, modelling, analytics, performance tuning, convergence, scalability improvements
Prefer candidates with solid, hands-on expertise in at least one or more of 3 domains, namely compute, network, storage
Experience in working with large customers such as Cloud Service Providers and global enterprise customers
Proven leadership in engaging customers with diverse technical disciplines in avenues such as Proof of Concept, Competitive evaluations, Early Field Trials etc
Direct experience in working with large customers and can operate with sense of urgency, own the problems and resolve it
Demonstrated leadership in network architecture, hands on experience in RoCEv2 Design, VXLAN-EVPN, BGP, and Lossless Fabrics
Proven ability to influence design and technology roadmaps, leveraging a deep understanding of datacenter products and market trends
Extensive hands-on Network deployment expertise and proven track record of delivering large projects on time. Cisco, Juniper or Arista experience is preferred
Direct, co-development/deployment experience in working with strategic customers/partners in bringing solutions to market
Excellent communication level from engineer to mid-management to C-level of audience

Job Responsibility

Collaborate with strategic customers on scalable designs involving compute, networking, storage environment, work with industry partners, Internal teams to accelerate the deployment, adoption of various AI/ML models
Engage system-level triage and at-scale debug of complex issues across hardware, firmware, and software, ensuring rapid resolution and system reliability
Drive the ramp of Instinct-based large scale AI datacenter infrastructure based on NPI base platform hardware with ROCm, scaling up to pod and cluster level, leveraging the best in network architecture for AI/ML workloads
Enhance tools and methodologies for large-scale deployments to meet customer uptime goals and exceed performance expectations
Engage with clients to deeply understand their technical needs, ensuring their satisfaction with tailored solutions that leverage your past experience in strategic customer engagements and architectural wins
Provide domain specific knowledge to other groups at AMD, share the lessons learnt to drive continuous improvement
Engage with AMD product groups to drive resolution of application and customer issues
Develop and present training materials to internal audiences, at customer venues, and at industry conferences

Senior Software Engineer, AI Platform and Enablement

We're building a next-generation AI-powered platform and web application for cre...

Location

United States , San Francisco

Salary:

180000.00 - 286000.00 USD / Year

Descript

Expiration Date

Until further notice

Requirements

Experience in deploying and managing AI models in production
Experience with the tools of large volume data pipelines like spark, flume, dask, etc.
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and container technologies (Docker, Kubernetes)
Knowledge of DevOps and MLOps best practices
Strong problem-solving abilities and excellent communication skills

Job Responsibility

Build, maintain, and standardize third-party model integrations, including consulting for other engineering teams with AI model integration needs
Design, implement, and maintain our AI infrastructure supporting our machine learning life cycle, including data ingestion pipelines, training developer experience and infrastructure, evaluation frameworks, and deployments / GPU infrastructure
Collaborate with Product Managers, Research Engineers, and AI Researchers to understand their infrastructure needs and ensure our AI systems are robust, scalable, and efficient
Optimize and scale our models and algorithms for efficient inference
Deploy, monitor, and manage AI models in production

What we offer

Generous healthcare package
401k matching program
Catered lunches
Flexible vacation time

Fulltime

New

Senior Principal AI Infrastructure Architect

The Senior Principal AI Infrastructure Architect is a highly skilled and advance...

Location

Italy , Milano

Salary:

Not provided

NTT DATA

Expiration Date

Until further notice

Requirements

Significant experience in a consulting, presales or architecture role within a large-scale (preferably multi-national) technology services environment, with a track record of leading AI infrastructure pursuits
Demonstrable experience designing and delivering production AI platforms — from single multi-GPU servers through to multi-rack training clusters and inference factories
Strong working knowledge of the AI hardware vendor landscape (NVIDIA, AMD, Intel, Dell, HPE, Lenovo, Supermicro, Cisco, Pure, VAST, WEKA, DDN, NetApp) and how to position partner ecosystems competitively
Proven ability to translate AI workload requirements (model size, parameter count, sequence length, throughput SLOs, latency targets) into accurate hardware bills of materials and sizing justifications
Significant client engagement and consulting experience, including client needs assessment, change management and the ability to identify whitespace for follow-on AI infrastructure and managed-services work
Significant business development and presales experience on infrastructure-led deals, ideally including sovereign AI, AI Factory or regulated-industry GenAI programmes
Strong understanding of how AI infrastructure integrates with business processes, applications, data platforms and existing enterprise architecture
Bachelor's degree or equivalent in Information Technology, Engineering, Computer Science or a related field
Deep, hands-on knowledge of AI hardware: GPU and accelerator portfolios (NVIDIA Hopper / Blackwell, AMD MI300/MI325, Intel Gaudi 3, emerging custom silicon), host CPU platforms (Intel Xeon, AMD EPYC, NVIDIA Grace), system topologies (HGX, DGX, MGX, OAM) and how each choice maps to specific AI workloads
Strong understanding of AI-class storage: parallel filesystems, all-flash NVMe platforms, S3-class object stores, checkpoint and dataset pipelines and the I/O patterns of large-scale training and inference (VAST, WEKA, DDN EXAScaler, Pure FlashBlade, NetApp ONTAP AI, Dell PowerScale)

Job Responsibility

Lead the end-to-end design of large, complex AI infrastructure solutions — covering accelerated compute (NVIDIA H100/H200/B200 and GB200 NVL72, AMD Instinct MI300X/MI325X, Intel Gaudi 3), CPU host platforms (Intel Xeon, AMD EPYC, NVIDIA Grace), high-throughput storage tiers and lossless AI fabric — for enterprise, sovereign AI and AI Factory clients
Architect reference designs built on NVIDIA DGX/HGX SuperPOD, Dell AI Factory with NVIDIA, Cisco Nexus HyperFabric AI, HPE / Lenovo / Supermicro accelerated compute and equivalent platforms, balancing single-node performance with cluster-scale efficiency
Size and validate GPU clusters against real workloads — foundation-model pre-training, distributed fine-tuning, RAG, real-time and batch inference — using the right combination of NVLink/NVSwitch domains, InfiniBand NDR/XDR or Ultra Ethernet / NVIDIA Spectrum-X fabrics and tiered NVMe and parallel storage (VAST, WEKA, DDN, Pure FlashBlade, NetApp ONTAP AI, Dell PowerScale)
Define the supporting datacenter design: high-density power (50–140 kW/rack), direct-to-chip and rear-door liquid cooling, structured cabling for AI fabrics and modular deployment models across on-prem, colo and sovereign-cloud footprints
Work closely with the sales team to drive the presales process for AI infrastructure pursuits — client discovery, technical workshops, proposal writing, executive presentations and bid defence
Translate clients' AI ambitions and business outcomes into a hardware and platform roadmap, positioning NTT DATA's end-to-end portfolio — silicon, systems, storage, fabric, MLOps stack and managed services — to land service-led AI solutions
Lead integration of compute, storage, networking, the AI software stack (CUDA, ROCm, Triton, NIM, NVIDIA AI Enterprise, Run:ai, Slurm, Kubernetes / Kubeflow) and managed-service operating models across multiple domains, delivery units and geographies
Build business cases, TCO and unit-economics models (cost per token, cost per training run, GPU-hour economics) and end-to-end transition roadmaps for cloud-to-private AI migrations and sovereign AI deployments
Define architectural principles for AI infrastructure — accelerator utilisation, data gravity, multi-tenancy, model lifecycle, energy efficiency — and apply them to influence architectural outcomes and governance
Develop As-Is, Vision, FMO and To-Be AI platform architectures, identify gaps and develop transition roadmaps

Fulltime

Senior Software Engineer- AI

Are you looking for an opportunity to work with the latest Azure offerings and p...

Location

India , Bangalore

Salary:

Not provided

Microsoft Corporation

Expiration Date

Until further notice

Requirements

7+ years of experience in Software Development
Strong programming expertise in one or more languages such as Python, Go, Java, or C#, with experience designing production-grade services and APIs
Experience building AI-powered applications, including integrating LLMs, implementing agent or Copilot workflows, and orchestrating multi-step AI interactions
Hands-on experience with LLM application frameworks and orchestration tools such as Semantic Kernel, LangChain, or similar agent frameworks
Familiarity with retrieval-augmented generation (RAG) architectures, vector databases, embeddings, and semantic search systems
Experience evaluating and improving model performance through prompt design, evaluation frameworks, fine-tuning, or feedback loops
Solid understanding of distributed systems concepts including scalability, reliability, observability, caching, and asynchronous processing
Experience deploying and operating AI workloads in cloud environments (preferably Azure), including containerized services and GPU-enabled infrastructure
Understanding of Responsible AI practices, including model governance, safety, privacy, and evaluation of AI behaviour in production systems
Ability to work across product, research, and engineering teams to translate product scenarios into scalable AI system architectures

Job Responsibility

Design, build, and operate scalable AI systems that power intelligent product experiences, including Copilot and agent-driven workflows
Architect and implement backend services that support multi-step AI interactions, including orchestration pipelines, context management, memory/state persistence, and tool execution
Integrate large language models (LLMs), APIs, and internal services to enable context-aware, human-in-the-loop experiences across customer scenarios
Build and maintain data and inference pipelines that support model training, fine-tuning, evaluation, and real-time inference across diverse data sources
Evaluate, benchmark, and tune AI/ML models (LLMs and traditional models) to meet product requirements for accuracy, latency, reliability, and safety
Implement robust retrieval, grounding, and knowledge integration mechanisms (e.g., RAG systems, semantic indexing, vector search) to power intelligent applications
Collaborate with product managers, software engineers, and researchers to translate product vision into production-ready AI capabilities and measurable outcomes
Ensure reliability, observability, and governance of AI systems, including monitoring model performance, data quality, and responsible AI practices
Build reusable platforms, APIs, and tools that enable teams to rapidly develop AI-powered features and self-service intelligent applications

Fulltime

Distinguished Engineer

As a Distinguished Engineer at Capital One, you will be a part of a community of...

Location

United States , San Jose, California; McLean, Virginia; New York, New York; San Francisco, California

Salary:

Not provided

Capital One

Expiration Date

Until further notice

Requirements

Bachelor's Degree
At least 7 years of experience in Software engineering

Job Responsibility

Articulate and evangelize a bold technical vision for your domain
Decompose complex problems into practical and operational solutions
Ensure the quality of technical design and implementation
Serve as an authoritative expert on non-functional system characteristics, such as performance, scalability and operability
Continue learning and injecting advanced technical knowledge into our community
Handle several projects simultaneously, balancing your time to maximize impact
Act as a role model and mentor within the tech community, helping to coach and strengthen the technical expertise and know-how of our engineering and product community
Design and drive the long-term technical roadmap for our Foundation Model Hosting platform, ensuring high throughput, ultra-low latency, and optimal GPU utilization across massive, multi-tenant workloads
Lead performance engineering across both the platform and model layers
You will pioneer the implementation of advanced techniques such as speculative decoding, continuous batching, kv-cache optimization (PagedAttention), and custom quantization strategies (FP8, INT4, AWQ)

What we offer

performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
comprehensive, competitive, and inclusive set of health, financial and other benefits

Fulltime

Senior AI Software Development Engineer

We are currently seeking a senior, experienced AI Software Engineer to join our ...

Location

Romania , Iasi; Brasov; Bucharest

Salary:

Not provided

AMD

Expiration Date

Until further notice

Requirements

Demonstrated experience delivering complex AI or software systems and influencing technical direction within a team
Strong understanding of AI/ML concepts and techniques, including deep learning, supervised and unsupervised learning, reinforcement learning, and probabilistic graphical models
Familiarity with popular ML frameworks and libraries, such as TensorFlow, PyTorch, Keras, and Scikit-learn
Proficient in programming languages such as Python, C++, and Java, with a strong focus on maintainable, high-quality production code
Familiarity with AMD's hardware (GPU, CPU, and APU) and software (ROCm, OpenCL, HIP) platforms is a plus, but not required
Strong analytical, problem-solving, and critical-thinking skills, with the ability to balance hands-on development with broader technical ownership
Excellent written and verbal communication skills, with the ability to effectively communicate complex concepts to a diverse audience
Bachelor’s or Master’s degree in Computer Science, Computer/Software Engineering or related technical discipline

Job Responsibility

Serve as a senior technical contributor, helping define system architecture, development standards, and best practices
Provide mentorship and technical guidance to other engineers through design discussions, code reviews, and knowledge sharing
Assist in the development of artificial intelligence models, algorithms, and systems tailored to specific project goals and requirements
Collaborate effectively with cross-functional teams, including product managers, researchers, hardware engineers, and software developers to support the development of comprehensive AI solutions
Learn and adapt to new techniques and methodologies to enhance product performance and develop new features
Optimize machine learning models for efficient deployment on AMD hardware and software platforms
Contribute to the process of monitoring the performance of deployed models, maintenance and updates, and troubleshooting any related issues
Stay current on the latest advancements in the fields of AI and machine learning, collaborating closely with colleagues to foster a culture of innovation

Fulltime

Senior Software Engineer, CoreAI Workload Engines

The CoreAI Workloads team builds the foundational inference engines and APIs tha...

Location

United States , Redmond

Salary:

119800.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science or related technical field and 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, Python, or equivalent experience.
Proven ability to design and operate large-scale, production inference services with high reliability and performance requirements, and to ship performance improvements safely via disciplined experimentation.
Strong skills in performance analysis: benchmarking, profiling, diagnosing regressions, and turning results into concrete engine/runtime changes.
Strong problem-solving skills and the ability to debug complex, cross layer systems issues.
Demonstrated technical leadership, including mentoring engineers, driving cross-team architectural alignment, and leveraging AI tools and AI-assisted workflows to accelerate engineering velocity and quality.
Hands-on experience with Kubernetes (building and operating services on k8s), including debugging production issues and designing platform abstractions (e.g., custom resources/controllers) and scheduling-aware deployments (e.g., node affinity, taints/tolerations, resource requests/limits).
Strong collaboration and communication skills, with the ability to work across organizational boundaries.

Job Responsibility

Optimize inference engines for OpenAI and open-source models by implementing and shipping performance/efficiency improvements across runtime, scheduling, and serving paths (latency, throughput, utilization, availability, and cost).
Run experiments end-to-end: formulate hypotheses, implement engine changes (including Python/PyTorch integration points where relevant), analyze results, and ship improvements behind guardrails.
Build and use experimentation capabilities for large-scale AI inference (experiment lifecycle, tracking, metric modeling, comparability standards, automated analysis) so the team can iterate quickly and safely.
Own serving availability and efficiency for Azure OpenAI Service workloads through tiered experimentation, lean segmentation, and multi-modal utilization across heterogeneous fleets—turning findings into shipped engine improvements.
Design and evolve inference serving architectures to improve utilization and latency using techniques such as disaggregated serving, multi-token prediction, KV offload/retrieval, and quantization—validated via staged rollouts and production guardrails.
Extend AI infrastructure abstractions to support elastic, heterogeneous inference engines reliably at scale (e.g., dynamic scaling across model families, modalities, and workload classes while maintaining isolation and SLOs).
Tune and scale inference engines across NVIDIA GPU generations (A100, H100, H200) for state-of-the-art OpenAI models, focusing on serving efficiency, utilization, and reliability (not hardware bring-up).
Partner with networking and storage teams to leverage high-performance interconnects (e.g., RDMA/InfiniBand-class fabrics such as RoCE over IB) for distributed inference, without owning low-level kernel/driver enablement.
Drive end-to-end features from design through production: observability, diagnostics, performance regression detection, and operational excellence for inference serving.
Influence platform architecture and technical direction across teams through design reviews, clear metrics, and technical leadership focused on experimentation velocity and production reliability.

What we offer

Benefits and other compensation

Fulltime

Senior AI Models MAD - Model Automation and Dashboarding Engineer

AMD is looking for a skilled and motivated software engineer to join the Model A...

Location

India , Hyderabad

Salary:

Not provided

AMD

Expiration Date

Until further notice

Requirements

Undergraduate and/or Master’s Degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
Strong C/C++/Python programming and software design skills, including debugging, performance analysis, and test design
Experience in test automation, CI/CD, and Linux scripting
Knowledge of GPU computing (HIP, CUDA, OpenCL)
Knowledge of Docker, Kubernetes, or Ansible for testing and deploying AI models and services at scale
Proficiency with version control (GitHub), testing strategies, code reviews, and collaborative software development
Strong written and verbal communication skills with a proactive approach to defining and driving development efforts

Job Responsibility

Enable and optimize key AI models (LLM, Vision, MultiModal, etc.) on AMD GPUs
Optimize AI frameworks like PyTorch, TensorFlow, etc., on AMD GPUs in upstream open-source repositories
Collaborate with internal GPU library teams and open-source framework maintainers to analyze, optimize, and integrate code changes upstream
Build and maintain automated functional and performance testing pipelines for AI models across ROCm-supported hardware using scalable tools
Develop tools and automation for continuous benchmarking and regression tracking across hardware generations and ROCm releases
Build and maintain real-time dashboards that report relevant performance, accuracy, and reliability metrics
Support public-facing MAD GitHub repositories and Docker releases, enabling the community to run and validate models on ROCm
Contribute to the design of portable, easy-to-use Python interfaces that support multi-node profiling, distributed workloads, and containerized deployments

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Senior AI Models GPU Deployment Software Engineer

Job Description

Job Responsibility

Requirements

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