Principal Machine Learning Engineer, Agentic AI Job at Zillow

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 Machine Learning Engineer

As a Principal Machine Learning Engineer, you will lead the architecture and dev...

Location

India , Hyderabad

Salary:

Not provided

Amgen

Expiration Date

Until further notice

Requirements

Bachelor’s degree in computer science, Engineering, Data Science, or a related field with 12 to 17 years of total experience
8+ years of experience in software engineering, machine learning engineering, or ML infrastructure
Strong experience building production ML systems or ML platforms
Hands-on experience with MLOps frameworks and tools such as MLflow / Equivalent - Model lifecycle management frameworks
Strong programming experience in Python and modern software engineering practices such as API Driven Architecture and Event based systems
Experience designing scalable distributed systems or cloud-native architectures
Experience deploying and operating machine learning models in production environments
Solid understanding of modern ML workflows including training, evaluation, deployment, monitoring, and retraining

Job Responsibility

Architect and build a scalable ML platform for training, deployment, and lifecycle management of ML, LLM, and Generative AI models
Lead development of infrastructure that supports production hosting of complex AI systems, including large-scale inference workloads
Design developer-friendly abstractions and automation that make it easy for researchers to build and deploy models within the Amgen ecosystem
Implement and evolve MLOps capabilities including experiment tracking, model versioning, CI/CD for ML, monitoring, and reproducibility using tools such as Databricks and MLflow
Build platform capabilities supporting Generative AI and emerging Agentic AI systems
Serve as the technical leader for a team of engineers, guiding architecture, design reviews, and engineering best practices
Partner with AI researchers, data scientists, and platform teams to translate cutting-edge AI research into reliable production systems
Evaluate and adopt emerging technologies across the modern AI stack including foundation models, vector databases, agent frameworks, and model serving systems
Champion AI-native engineering practices, leveraging tools like GitHub Copilot, Codex, and AI-assisted development workflows
Contribute to the broader strategy and evolution of the Enterprise AI Platforms ecosystem

Principal Machine Learning Engineer

This is a high-leverage leadership role that spans architecture, execution, and ...

Location

India , Bengaluru

Salary:

Not provided

Ema

Expiration Date

Until further notice

Requirements

Bachelor’s or Master’s (or PhD) degree in Computer Science, Machine Learning, Statistics, or a related field
A strong track record (usually 10-12+ years) of applied experience with ML techniques, especially in large-scale settings
Experience building production ML systems that operate at scale (latency / throughput / cost constraints)
Experience in Knowledge retrieval and Search space
Exposure in building Agentic Systems and Frameworks
Proficiency in relevant programming languages (e.g. Python, C++, Java) and ML frameworks (TensorFlow, PyTorch, etc.)
Strong understanding of the full ML lifecycle: data pipelines, feature engineering, model training, serving, monitoring, maintenance
Experience designing systems for monitoring, diagnostics, logging, model versioning, etc.
Deep knowledge of computational trade-offs: distributed training, inference, optimizations (e.g. quantization, pruning, batching)
Excellent communication skills

Job Responsibility

Lead the technical direction of GenAI and agentic ML systems that power enterprise-grade AI agents — spanning reasoning, retrieval, tool use, and integrations across various SaaS products
Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval (RAG), agent orchestration, and evaluation — ensuring robustness, latency efficiency, and continuous learning
Define and own the multi-year ML roadmap for GenAI infrastructure — including agent frameworks, RAG systems, world-class evaluation loops, and integration with MCP, browser, and vision pipelines
Identify and integrate cutting-edge ML methods / research (deep learning, large models, recommender systems, LLMs, etc.) into Ema’s products or infrastructure
Research, prototype, and integrate cutting-edge ML and LLM advancements (reasoning, memory architectures, multi-modal perception, long-context models, autonomous agents) into the platform
Optimize trade-offs between accuracy, latency, cost, interpretability, and real-world reliability across the agent lifecycle — from prompt design to orchestration and execution
Champion engineering excellence — drive observability, reproducibility, versioning, testing, and bias-aware development across ML and agentic systems
Mentor and elevate senior engineers and researchers, fostering a culture of scientific rigor, experimentation, and system-level thinking
Collaborate cross-functionally with product, infra, and research teams to align ML innovation with enterprise needs — enabling secure integrations, privacy-aware deployments, and scalable use cases
Influence data strategy — guide how retrieval indices, embeddings, structured/unstructured corpora, and feedback loops evolve to improve grounding, factuality, and reasoning depth

Fulltime

Sr. Distinguished AI Engineer (Agentic AI Platform)

At Capital One, we are creating responsible and reliable AI systems, changing ba...

Location

United States , San Jose, California; San Francisco, California

Salary:

343400.00 - 392000.00 USD / Year

Capital One

Expiration Date

Until further notice

Requirements

Bachelor's degree in Computer Science, Engineering, or AI plus at least 10 years of experience developing AI and ML algorithms or technologies, or Master's degree plus at least 8 years of experience developing AI and ML algorithms or technologies
At least 10 years of experience programming with Python, Go, Scala, or Java
9 years of experience deploying scalable and responsible AI solutions on cloud platforms
2+ years of experience supporting Agentic Frameworks
2+ years of experience with LLMOps
8+ years of experience designing mission-critical machine learning platforms
2+ years of experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems
Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level
Experience developing AI and ML algorithms or technologies using Python, C++, C#, Java, or Golang
Master's degree in Computer Science, Computer Engineering, or relevant technical field

Job Responsibility

Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
Contribute to the north star platform architecture, continuously publishing and refining living diagrams and canonical APIs
Standardizing and automating agentic workflows
Contribute to crafting an end to end GenAI SDK, CLI and starter kits
Help bring together a vision of central guardrail services
Collaborate with cross organization architects to drive end to end performance
Accelerate innovation by incubating proof of concepts and driving RFCs
Own central Helm charts, operators and CRDs that auto scale agents to hit tenant SLAs
Coach and evangelize - hosting architecture office hours, mentoring Staff, Principal and Senior engineers, authoring technical design documents and blogs and representing Capital One at Tier1 AI conferences

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

Principal Ai Engineer - Enterprise Ai Solutions

Our Mission: At Palo Alto Networks®, we’re united by a shared mission—to protect...

Location

United States , Santa Clara

Salary:

185200.00 - 299475.00 USD / Year

Palo Alto Networks

Expiration Date

Until further notice

Requirements

Bachelor's degree in a related field or equivalent military experience required
12+ years of related experience, or a Master's degree with 8 years of experience, or a PhD with 5 years of experience
Proven expertise in designing and building complex, enterprise-grade AI/ML platforms and applications
Direct hands-on experience with Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI systems
Strong programming skills in languages such as Python, Java, or Go and proficiency in modern AI/ML frameworks (e.g., TensorFlow, PyTorch)
Extensive hands-on experience with distributed systems architecture, streaming data platforms, and cloud AI/ML platforms (e.g., GCP Vertex AI, AWS SageMaker)

Job Responsibility

Lead the applied AI solution design and architecture, breaking down ambiguous business problems into concrete, actionable AI solution designs
Drive the hands-on development and implementation of key AI components, supporting both traditional and Generative AI model development and deployment
Contribute significantly to the detailed design of large-scale, distributed AI/ML systems, ensuring performance, reliability, and security
Proactively collaborate with executive leadership, data science, engineering, and product stakeholders to translate business use-cases into scalable AI solutions
Provide technical leadership and mentorship to other AI/ML engineers, fostering a culture of engineering excellence and hands-on experimentation
Lead the implementation and continuous improvement of MLOps pipelines, including automated model training, versioning, deployment, and monitoring
Champion and enforce design standards, patterns, and best practices for scalable and secure development of AI applications across various teams
Actively research and evaluate cutting-edge AI/ML techniques, algorithms, and models to identify opportunities for platform enhancement and new solution development

What we offer

Restricted stock units
Bonus

Fulltime

Principal AI Engineer – Vivado EDA Tools

We are seeking a skilled AI expert to join our new team focused on integrating A...

Location

United States , Boxborough

Salary:

212000.00 - 318000.00 USD / Year

AMD

Expiration Date

Until further notice

Requirements

Extensive experience with AI/ML initiatives and a proven track record of deploying production AI systems
Deep expertise in Large Language Models, fine-tuning, and prompt engineering
Experience designing agentic AI solutions and multi-agent systems
Strong background in EDA tools and workflows, preferably with Synopsys experience
Proficiency in MLOps, model deployment, and lifecycle management
Advanced skills in Python, C++, and AI/ML orchestration frameworks
In-depth knowledge of FPGA design methodologies and tool development

Job Responsibility

Design and implement Large Language Model integrations for FPGA design assistance
Develop Agentic AI systems for automated design optimization
Conduct research and development of novel AI applications in EDA tools

Fulltime

Principal AI Engineer

The Principal AI Engineer is part of our AI Centre of Excellence team at the for...

Location

United States , Boston, MA; Austin, TX

Salary:

236500.00 - 320000.00 USD / Year

Rapid7

Expiration Date

Until further notice

Requirements

Significant hands-on experience in applied AI, able to independently design and build AI models and systems
Strong Python skills and practical expertise with: scikit-learn for conventional ML and data science
PyTorch, TensorFlow, or Keras for deep learning
AWS Bedrock, SageMaker, HuggingFace, or Transformers for LLM-based solutions
CrewAI (or similar frameworks) for agentic AI projects
Pandas/NumPy for data processing and transformation
Intuition for selecting the right models and approaches for different use cases
Cybersecurity expertise is a plus, but experience from other domains (e.g., finance, healthcare, marketplaces, insurance, legal, social media) is also highly valuable
Positive, can-do, solution-oriented mindset with persistence in tackling complex challenges
Bias for action and delivery, balancing speed with care

Job Responsibility

Develop and ship AI systems with a focus on delivering customer value
Tackle a diverse range of complex problems, often reframing existing solutions with new perspectives
Take ownership of multiple projects as the principal go-to expert
Act as a first-class stakeholder throughout the AI R&D process
Build strong cross-functional relationships with engineering, UX, product management, and customer research teams
Mentor colleagues with less experience, acting as a trusted technical resource

Principal AI Engineer

As a Principal AI Engineer on the AI Foundations team, you are an established su...

Location

Singapore , Singapore

Salary:

Not provided

Mastercard

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, Applied Mathematics, or related technical field
advanced degree preferred
Strong foundation in software engineering, distributed systems, and applied machine learning relevant to production AI systems
Demonstrated understanding of responsible AI, model/system risk, privacy/security considerations, and governance requirements for enterprise deployments
Demonstrated, sustained ownership of production AI/ML systems, including design, build, deployment, and ongoing lifecycle operations
Real-world experience shipping complex agentic systems into production, including multi-agent coordination and multi-tool integration with safe action policies
Hands-on experience building production pipelines for evaluation, monitoring, versioning, and continuous improvement (including retraining or policy/guardrail updates)
Proven ability to define and operationalize observability and reliability practices for agentic systems (traceability, telemetry, SLOs, incident management)
Track record of influencing architecture and standards across multiple teams or programs, and mentoring engineers to raise overall engineering rigor

Job Responsibility

Serve as an established subject matter expert in AI Engineering, influencing stakeholders and shaping technical direction across multiple initiatives
Architect, design, develop, and maintain advanced AI/ML systems, with emphasis on complex agentic solutions (multi-agent orchestration, tool/function-calling, memory, reflection/self-correction, and autonomy policies)
Lead production implementation of agentic AI systems, including scalable training and evaluation pipelines, deployment frameworks, and runtime orchestration patterns
Define and implement safe tool-use patterns: structured outputs, robust error handling, permissioning and auditability, human-in-the-loop (HITL) approval steps for sensitive actions, and guardrail enforcement
Establish end-to-end AgentOps/LLMOps practices for agentic systems: release pipelines for prompts/tools/policies, canary strategies, safe rollback mechanisms, and continuous regression/safety evaluations as release gates
Build and optimize data ingestion, preprocessing, feature/embedding engineering, and retrieval/memory workflows to improve grounding quality and reduce failure modes
Own production observability for agentic systems: trace capture, cost/token telemetry, latency and reliability SLOs, and incident response practices for agent failures
Implement drift detection and performance decay monitoring (data drift, concept drift), and automate model/agent retraining, policy updates, and redeployment to maintain output quality over time
Drive measurable improvements in system effectiveness, safety, and efficiency by defining success metrics (task success, intervention rate, policy violations, cost and latency per task) and continuously improving evaluation coverage
Mentor and grow senior and junior engineers through design reviews, code reviews, hands-on coaching, and the creation of reusable patterns, playbooks, and standards for agentic delivery

Fulltime

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Principal Machine Learning Engineer, Agentic AI

Job Responsibility

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