Principal MLOps Architect Job at Intellias

Principal AI Architect

As the Principal AI Architect for Teradata AI Studio, you will define the techni...

Location

India , Bengaluru, Karnataka, India | Hyderabad, Telangana, India

Salary:

Not provided

Teradata

Expiration Date

Until further notice

Requirements

10+ years of software engineering experience, including 3+ years in a senior architect or principal engineer role with platform-wide technical scope
Demonstrated expertise designing AI/ML platforms or developer tools: model serving infrastructure, feature stores, experiment tracking, MLOps pipelines, or AI agent development environments
Deep understanding of LLM integration patterns: RAG architectures, fine-tuning pipelines, evaluation frameworks, and agent tool-calling interfaces
Experience with enterprise data platforms (Teradata Vantage, Snowflake, Databricks, or equivalent) at sufficient depth to architect against their APIs, security models, and performance characteristics

Job Responsibility

Define the technical architecture of Teradata's end-to-end AI development environment
Set the architectural direction for how AI Studio integrates with Teradata Vantage's query engine, model registry, feature store, and agent harness
Establish the patterns for how enterprise customers build trustworthy AI workflows
Ensure that AI Studio is the most capable, governed, and scalable AI development environment in the market

What we offer

Flexible work model
People-first culture
Focus on well-being
Commitment to fostering an inclusive environment

Fulltime

Principal Ai Architect

At Teradata, we believe that people thrive when empowered with better informatio...

Location

United States , California

Salary:

217300.00 - 325900.00 USD / Year

Teradata

Expiration Date

Until further notice

Requirements

10+ years of software engineering experience, including 3+ years in a senior architect or principal engineer role with platform-wide technical scope
Demonstrated expertise designing AI/ML platforms or developer tools: model serving infrastructure, feature stores, experiment tracking, MLOps pipelines, or AI agent development environments
Deep understanding of LLM integration patterns: RAG architectures, fine-tuning pipelines, evaluation frameworks, and agent tool-calling interfaces
Experience with enterprise data platforms (Teradata Vantage, Snowflake, Databricks, or equivalent) at sufficient depth to architect against their APIs, security models, and performance characteristics
Experience building developer-facing platforms — SDKs, APIs, or IDEs — that external developers adopt and extend
Familiarity with open-source AI development tools: MLflow, Weights & Biases, Hugging Face, LangChain, LangGraph, or comparable
Understanding of enterprise AI governance requirements: model lineage, data access controls, audit logging, and responsible AI guardrails
Experience with cloud-native architecture (AWS, Azure, GCP) and containerized ML workloads (Kubernetes, Docker)
Strong cross-functional influence: you can drive alignment across engineering, product, and customer-facing teams without formal authority
A portfolio of architectural decisions — RFCs, design docs, or open-source work — that demonstrates your approach

Job Responsibility

Define the technical architecture of Teradata's end-to-end AI development environment — the platform where data scientists, ML engineers, and AI developers build, test, deploy, and monitor AI and agentic applications on top of Vantage
Set the architectural direction for how AI Studio integrates with Teradata Vantage's query engine, model registry, feature store, and agent harness
Establish the patterns for how enterprise customers build trustworthy AI workflows — from data preparation through model deployment to agent-driven automation
Ensure that AI Studio is the most capable, governed, and scalable AI development environment in the market
Ship architectural decisions that other engineers can build on with confidence
Drive customer adoption of AI Studio at scale

What we offer

Healthcare
life and disability insurance plans
401(k)-retirement savings plan
time-off programs

Fulltime

Principal AI Architect

As a Principal AI Architect, you will define and drive the end-to-end Cloud + AI...

Location

United States , Multiple Locations

Salary:

139900.00 - 274800.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
OR equivalent experience.
Master's Degree in Computer Science, Engineering or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
OR equivalent experience.
10+ experience in software engineering or solution architecture, with demonstrable success building and operating complex systems.
10+ experience designing, implementing, and optimizing AI solutions encompassing data pipelines, model training and serving, and production MLOps.
10 + years of full-stack development across frontend, backend, and cloud infrastructure, with a proficient command of data engineering, AI/ML systems, and deployment architectures.
2+ project experience with NLP and LLM-based systems.
Proficient coding skills in C#, Python, JavaScript, and React.

Job Responsibility

Own the reference architecture and technical roadmap for WWL’s AI/Agentic platform capabilities (e.g., orchestration, tools/plugins, memory, retrieval, evaluation, observability, governance).
Translate skilling business objectives into platform investments and architectural decisions, balancing speed-to-value with security, compliance, cost, and long-term maintainability.
Establish clear architectural guardrails and decision frameworks (e.g., “build vs. buy,” “Copilot Studio vs. Foundry,” “RAG vs. fine-tune,” “central vs. federated patterns”).
Lead architecture/design reviews for major initiatives
drive alignment on system boundaries, contracts, dependency management, and resiliency.
Define and standardize architecture patterns (multi-tenant SaaS, event-driven architectures, secure data access, model routing, agent safety controls).
Create reusable templates, “golden paths,” and reference implementations to accelerate engineering delivery across teams and reduce fragmentation.
Embed Responsible AI principles into agentic solution design (human-in-the-loop, safety mitigations, evaluation, transparency, and auditability).
Partner with security and compliance stakeholders to ensure services meet required controls and operational standards, and to drive alignment between policy intent and implementation.
Define secure patterns for prompt/data handling, secrets management, identity, and access governance for AI systems.

Fulltime

Principal AI Software Architect

Principal Artificial Intelligence (AI) Software Architect leads the design, deve...

Location

United States , St. Louis

Salary:

Not provided

VantageLinks

Expiration Date

Until further notice

Requirements

Bachelor's degree in the field of computer science, information systems, or computer engineering or equivalent experience
7+ years of experience in software architecture and engineering, with at least 2 years focused on AI/ML systems
5+ years of hands-on experience with application development tools, including Visual Studio .NET
5+ years of hands-on experience with relational database technology, preferably MS SQL Server

Job Responsibility

Architects and leads the development of AI-driven platforms and scalable software systems aligned with strategic business goals
Collaborates cross-functionally with engineering, product, and business teams to define use cases and deliver impactful solutions
Evaluates and selects AI and software technologies, frameworks, and tools for optimal performance, scalability, and maintainability
Establishes and promotes best practices in AI model development, MLOps, software engineering, DevOps, and system architecture
Mentors engineering teams, fostering a culture of innovation, excellence, and continuous improvement
Drives technical strategy and long-term planning, aligning engineering efforts with enterprise objectives
Ensures compliance with data privacy, security, and ethical standards in all AI and software initiatives
Champions engineering excellence through code reviews, automated testing, CI/CD, and agile methodologies
Other duties as needed or assigned

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

Principal AI Ops Architect

Scale’s rapidly growing Global Public Sector team is focused on using AI to addr...

Location

Qatar; United Kingdom , Doha; London

Salary:

Not provided

Scale

Expiration Date

Until further notice

Requirements

6+ years in a high-impact technical role (SRE, FDE or MLOps) with experience in the public sector
Familiarity with international government security standards and the complexities of deploying sovereign AI
Proven experience maintaining production-grade applications with a deep understanding of the full request lifecycle-connecting frontend/API layers to the backend and AI core
Proficiency in coding and the modern AI infrastructure, including Kubernetes, vector databases, agentic development, and LLM observability tools
Ownership: You treat every production deployment as your own. You race toward solving hard problems before the customer even sees them
Reliability: You understand that in the public sector, a model failure may be a risk to public safety or privacy
Customer communication: The ability to explain to a high-ranking official why the performance of the system has degraded and how we are fixing it

Job Responsibility

Own the production outcome: Take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies
Ensure Full-Stack integrity: Oversee the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment
Scale the feedback loop: Build automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring the right levels of reliability
Navigate global compliance: Manage the technical lifecycle within diverse regulatory frameworks
Incident command: Lead the response for production issues in mission-critical environments, ensuring rapid resolution and building the guardrails to prevent them from happening again
Bridge the gap: Translate deep technical performance metrics into clear insights for senior international government officials
Drive product evolution: Partner with our Engineering and ML teams to ensure the lessons learned in the field directly influence the technical architecture and decisions of future use cases

New

Principal Machine Learning Engineer - Forecasting

We are seeking a Principal Machine Learning Engineer to join the Forecasting tea...

Location

India , Hyderabad

Salary:

Not provided

Amgen

Expiration Date

Until further notice

Requirements

Degree and 12+ years of experience in machine learning engineering, software engineering, data science engineering, or a related quantitative discipline.
10+ years of professional experience building, deploying, and operating production ML, AI, data, or software systems, including significant experience as a technical lead on complex, cross-functional initiatives.
Demonstrated track record of designing or architecting new and existing systems with emphasis on reliability, scale, security, maintainability, and operational excellence.
Deep hands-on experience with the full ML engineering lifecycle, including data pipelines, feature engineering, experimentation, model training, model integration, testing, deployment, monitoring, evaluation, observability, and continuous improvement.
Strong experience deploying forecasting, probabilistic, Bayesian, predictive, NLP, deep learning, or LLM-based systems in production environments.
Experience building or integrating AI systems, including LLM-powered applications, agentic workflows, retrieval or information-retrieval systems, evaluation frameworks, and human-in-the-loop review patterns.
Strong object-oriented programming skills in Python and SQL, with experience using modern ML and software development frameworks such as scikit-learn, PyTorch, TensorFlow/JAX, Spark, Ray, MLflow, Airflow/Prefect/Dagster, FastAPI, or equivalent technologies.
Experience with cloud platforms and distributed systems, including containerization, CI/CD, infrastructure-as-code, model serving, workflow orchestration, batch and streaming data processing, and production support.
Strong software engineering fundamentals, including system design, architecture trade-off analysis, testing strategies, code reviews, source control, build and release processes, performance optimization, and maintainability.
Demonstrated ability to communicate technical strategy, system tradeoffs, and delivery risks to technical and non-technical stakeholders, including senior leaders, product/program owners, scientists, and business partners.

Job Responsibility

Define and drive the technical strategy for enterprise forecasting and AI decision systems, aligning architecture, reusable platforms, and delivery roadmaps to Amgen's planning, supply, commercial, manufacturing, operations, and patient-focused priorities.
Partner with data scientists, product and program leaders, operations, commercial, manufacturing, supply chain, finance, and other business stakeholders to translate ambiguous requirements into shipped software and measurable business outcomes.
Architect, build, and scale production ML, LLM, and agentic AI systems that combine forecasting, predictive analytics, simulation, optimization, and autonomous or semi-autonomous workflow automation.
Productionize advanced statistical, Bayesian, deep learning, and machine learning models, including training, validation, inference, serving, evaluation, lifecycle management, and governed deployment.
Lead development of AI agent components that automate complex forecasting and operational workflows across multiple systems, decision points, datasets, and user groups while preserving appropriate human-in-the-loop review and escalation patterns.
Design secure integrations across enterprise APIs, databases, analytics platforms, workflow systems, cloud services, and AI orchestration patterns to enable multi-system decision support and scalable automation.
Establish robust MLOps and AI engineering capabilities, including model versioning, CI/CD, automated retraining, performance monitoring, observability, drift detection, service-level reliability, rollback strategies, and operational runbooks.
Implement guardrails, model and agent evaluation frameworks, auditability, explainability, responsible AI controls, and human-in-the-loop operating models for production AI systems in high-impact and regulated business contexts.
Research and evaluate state-of-the-art open-source, vendor, and internal tools related to forecasting, LLMs, AI agents, MLOps, model optimization, model serving, and scalable AI infrastructure for potential application to Amgen business problems.
Provide principal-level technical mentorship, design review leadership, and engineering standard-setting across teams, promoting code quality, documentation, reproducibility, testing, security, privacy, maintainability, and operational excellence.

Fulltime

Principal Data Science Analyst

We are seeking a Principal Data Science Analyst to design and implement the AI/M...

Location

Slovakia , Bratislava

Salary:

3850.00 EUR / Month ▼

BioLife Plasma Services

Expiration Date

Until further notice

Requirements

Master’s or Ph.D. in Data Science, CS, Engineering, or related STEM
10+ years in AI/data science roles, including demonstrated technical and people leadership
Advanced ML/AI expertise experience (Classical ML: KNN, SVM, Random Forest/GBMs etc.). Deep learning: CNNs/RNNs/GRU, autoencoders, Backprop/Autodiff and experience with MLOps at scale
Strong programming skills (Python, SQL) and familiarity with distributed computing (Spark, Kubernetes)
Experience in regulated environments, specifically: pharma preferred

Job Responsibility

Lead global AI/ML initiatives across 20+ manufacturing facilities in alignment with Manufacturing Sciences strategic priorities
Architect AI platforms integrating real-time manufacturing data with cloud MLOps environments
Drive innovation in AI/ML manufacturing optimization and autonomous process control
Establish standards for model governance, bias mitigation, and regulatory compliance
Mentor data scientists and collaborate with Manufacturing Sciences SMEs, IT, engineering and quality business functions
Translate emerging AI/ML trends into practical applications that support Manufacturing Sciences decision-making and operational goals

What we offer

Cafeteria
Medical - High standard medical care
Bridge days
Summer Hours
Multisport Card

Fulltime

Select Country

Principal MLOps Architect

Job Description

Job Responsibility

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