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Today

Staff Machine Learning Engineer

Estee Lauder New York, NY

New York, NY

Today

$119k - $197k/yr Full time Backend Developer Agentic Ai AI

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Job Description

The Staff Machine Learning Engineer (player-coach) will design, build, test, and deploy production AI products and the underlying platform that enables agentic, LLM-powered capabilities on Google Cloud. The role combines applied machine learning with software engineering rigor, including support for stateful and transactional systems, and includes mentoring responsibilities.

Key Responsibilities

Design, build, test, and deploy production agentic and LLM-powered products end-to-end on Google Cloud, including the shared platform, tooling, and harnesses beneath them.
Apply strong software-engineering rigor to services that involve stateful and transactional (OLTP) systems, with focus on data modeling, concurrency, idempotency, reliability, observability, and testing.
Use classical ML, statistics, and experimentation when appropriate, including predictive modeling, statistical inference, hypothesis testing, and causal or incrementality measurement.
Build scalable automation tools for domains such as recommendation and personalization, conversational and customer experiences, and measurement and incrementality.
Produce design documents, maintain standards for clean code and documentation, and review designs and deliverables.
Mentor and develop engineers and scientists, including participation in early-career and student mentorship efforts.
Collaborate with engineering, product, and domain-expert teams on cross-functional automation, measurement, and modeling initiatives.
Participate in ELC’s diversity and inclusion agenda.

Required Qualifications

BS/BA in a quantitative or technical field (for example, Computer Science, Statistics, Mathematics, Physics, Engineering, Operations Research, Economics) or equivalent practical experience; a graduate degree is a plus.
5+ years of experience (or 3+ years with a graduate degree) across Software Engineering, Machine Learning, and/or Data Science.
Strong general software engineering in Python, including clean API and service design, data-structures and algorithms, and a disciplined approach to testing. Experience building and operating production-grade services, including stateful/transactional systems and databases using SQL and/or NoSQL.
Production experience deploying and operating LLM-powered or agentic systems (examples: retrieval-augmented generation, agents and tool use, structured outputs, evaluation, safety) or a strong production ML background with a clear interest in transitioning into this space.
Experience deploying and operating systems on a major cloud platform, with Google Cloud as a plus, and sound data-processing practices at scale.
Experience using agentic/AI-assisted development tools, or a strong willingness to adopt them with judgment to protect quality.
A track record of mentorship, continuous learning mindset, bias for simplicity, and collaborative shared-ownership working style.

Technologies

Python
Google Cloud
SQL, NoSQL
Retrieval-augmented generation
LLM-powered systems
Agentic systems
Vertex AI
Cloud Run
BigQuery
Firestore
Pub/Sub
FastAPI
Container-based CI/CD
Model Context Protocol (MCP)

Benefits

Health insurance coverage (medical, dental, and vision insurance)
Wellness and family support programs
Life and disability insurance
Retirement savings plans
Paid leave programs
Education-related programs
Paid holidays and vacation time
Bonus program (highly competitive), with the possibility for overachievement
Participation in the share incentive plan

Preferred / Strong Plus

Depth in classical ML, data science, and statistics, including statistical modeling, hypothesis testing and experimentation, causal inference and incrementality, and predictive modeling.
Hands-on experience in NLP, agent frameworks, or advanced statistical methods.
Familiarity with the stack, including Google Cloud (Vertex AI, Cloud Run, BigQuery, Firestore, Pub/Sub), async Python (FastAPI), container-based CI/CD, and the Model Context Protocol (MCP).
Experience designing a technical roadmap and leading execution in a business environment.

Compensation and Pay Range

The anticipated base salary range is $119,300.00 to $196,600.00 per year.
Exact compensation depends on factors such as experience, skills, education, and budget.
Salary range may vary based on geographic location.
In addition to base salary, the position is eligible for participation in a highly competitive bonus program and the share incentive plan.

Location

New York, NY (onsite)

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Today

AI Engineer

SCIGON Chicago, IL

Chicago, IL

Today

$52 - $70/hr Full time AI Artificial Intelligence Azure Ai

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Job Description

SCIGON is seeking an Applied AI Engineer to design, deploy, and operationalize enterprise AI capabilities. The position centers on implementing LLM-powered solutions with governance, security, and integration into existing systems, with an emphasis on production readiness and ongoing support.

Role Responsibilities

Design, deploy, and manage reusable AI capabilities within enterprise AI platforms, including skills, tools, and plug-in functionality.
Own the full lifecycle of AI enablement solutions, from intake and configuration through deployment, governance, ongoing support, and optimization.
Configure foundation models such as Claude, Gemini, and comparable technologies to enable enterprise automation and business workflows.
Deliver AI solutions from concept through production implementation, ensuring scalability, security, and operational readiness.
Integrate existing enterprise systems, services, and approved tools using available connectivity frameworks and protocols.
Collaborate with Security, Cloud, and Infrastructure teams to implement access controls, identity management practices, and credential governance.
Ensure compliance with AI governance standards, including auditability, monitoring, risk controls, and operational guardrails.
Partner with engineering, automation, data, and business teams to identify opportunities and deliver AI-driven capabilities.

Required Qualifications

Proven experience developing, publishing, and managing Claude Skills or plug-ins in a production environment.
Demonstrated success deploying AI capabilities that are actively used by business stakeholders.
Ability to contribute quickly with minimal onboarding and ramp-up time.
Experience building and deploying solutions in regulated or highly governed enterprise environments.
Strong understanding of security controls, compliance requirements, access management, and operational governance.
Comfort working within structured delivery processes and change-control frameworks.
Strong communication and stakeholder management skills.
Capability to translate business challenges into practical AI-enabled solutions.
Experience collaborating with technical and non-technical audiences.
5+ years of experience in software engineering, automation engineering, or a related technical discipline.
At least 2 years designing and deploying production-grade solutions powered by large language models.
Strong proficiency in Python development and API-based integrations.
Experience with enterprise software integration patterns and distributed systems.
Solid engineering practices, including testing, source control, observability, monitoring, and supportability.
Hands-on experience with modern LLM ecosystems, including prompt engineering, model configuration, tool integration, and function execution.
Experience working in public cloud environments such as Azure, Google Cloud Platform, Vertex AI, or equivalent technologies.
Ability to evaluate AI use cases pragmatically, including determining when traditional engineering approaches may be more effective.
Self-directed ability to independently lead technical initiatives in a fast-moving environment.

Technologies

Claude, Gemini
Python
Azure
Google Cloud Platform
Vertex AI
LLM ecosystems
Model Context Protocol (MCP)
RAG architectures
Vector databases, embeddings
Semantic search solutions
RBAC

Preferred Qualifications

Experience building AI agents and multi-agent workflows.
Familiarity with orchestration platforms and agent frameworks.
Understanding of Model Context Protocol (MCP) implementations and agent-to-agent integrations.
Experience with RAG architectures, vector databases, embeddings, and semantic search solutions.
Knowledge of modern identity and access management concepts, including RBAC, service accounts, agent identities, and least-privilege models.
Prior experience supporting organizations operating in highly regulated industries such as insurance, financial services, healthcare, or similar sectors.

Location and Compensation

Location: Chicago, IL (onsite)

Compensation: USD 52 - 70 per hour

Minimum Experience: 5 years

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