AI/ML Lead

Jobgether; United States; On-site; USD 151,000 - 242,000; Full time

Job summary

Jobgether is seeking an AI/ML Lead in the United States to build and scale internal AI capabilities. As the first AI engineer on the team, you will implement production-grade AI solutions focused on personalized member experiences using RAG, knowledge graphs, and recommender systems.

You will own data and retrieval pipelines, integrate large language models, and establish robust engineering practices for secure and scalable AI systems, collaborating across architecture, product, data, security,

Qualifications

• Bachelor's degree in a technical field or equivalent professional experience.
• 8+ years of professional software/data experience with 2+ years building/operating production AI/ML solutions.
• Experience delivering enterprise-scale AI systems, preferably in cloud environments like Azure.

Responsibilities

• Architect, build, and operate production AI solutions with a focus on a personalized member experience using RAG, knowledge graphs, and recommendations.
• Lead AI platform engineering, including AI components, embeddings, retrieval pipelines, and LLM-driven apps.
• Define vendor engagement criteria, validate production readiness, and transition vendor components to internal ownership.

Skills

Python

LLM integration

RAG systems

MLOps

Data pipelines

Graph databases

Team leadership

Education

Bachelor's degree

Tools

Azure DevOps

GitHub Actions

Docker

Kubernetes

Azure Data Factory

LangChain

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI/ML Lead based in the United States.

This is an opportunity for a senior, hands-on AI engineer to help build and scale an organization’s internal AI engineering capabilities. As the first engineer on a growing AI implementation team, you will develop production-ready AI solutions while progressively bringing work currently handled by external vendors in-house. Your initial focus will be a personalized member experience powered by retrieval-augmented generation (RAG), knowledge graphs, and AI-driven recommendations. You will build reliable data and retrieval pipelines, integrate large language models, and establish the engineering practices needed to operate AI systems securely and efficiently. Working closely with architecture, product, data, security, and engineering teams, you will translate strategic AI direction into maintainable, scalable implementations. This role combines hands-on development, technical oversight, and team-building responsibilities, with a strong emphasis on production stability, responsible AI, and long-term technical ownership.

Accountabilities

As AI/ML Lead, you will drive the engineering implementation of AI solutions, strengthen internal technical capabilities, and ensure that production systems meet established architecture, security, and governance standards. You will collaborate across teams to deliver reliable AI experiences while reducing dependence on external development partners.

• AI Platform Engineering: Develop, integrate, and maintain production AI solutions, initially focusing on a personalized member experience that combines RAG, knowledge graphs, and recommendation systems.
• Build and maintain core AI components, including knowledge graph ontologies and configurations, embedding and retrieval pipelines, recommendation logic, and large language model (LLM)-powered and agentic application components.
• Implement APIs and service contracts connecting AI capabilities with digital experiences and enterprise business systems.
• Design reusable, maintainable components that support long-term internal ownership and future expansion of the AI portfolio.
• Vendor Oversight and Insourcing: Serve as the primary internal technical counterpart for external AI development vendors and contractors.
• Participate in architecture reviews and evaluate vendor code, pull requests, RAG implementations, knowledge graph solutions, and LLM integrations against established technical standards.
• Define acceptance criteria for vendor deliverables, validate production readiness, and elevate technical risks or delivery misalignment to architecture and IT leadership.
• Lead structured knowledge-transfer activities and transition vendor-developed components to internal ownership according to a planned timeline.
• AI Engineering Practices and Operations: Implement and operate MLOps and LLMOps practices, including CI/CD pipelines, model and prompt versioning, evaluation, monitoring, drift detection, and token cost tracking.
• Establish consistent engineering standards for code reviews, technical documentation, operational runbooks, and reusable components.
• Develop practices that encourage knowledge sharing and ensure critical expertise is distributed across the implementation team.
• Implement security, responsible AI, and auditability controls defined by architecture and security stakeholders.
• Data Infrastructure and AI Enablement: Build and maintain reliable data ingestion and transformation pipelines from sources such as CRM systems, third-party content and learning platforms, and external workforce or market data providers.
• Establish data contracts with source-system owners and implement data quality checks and monitoring.
• Identify weaknesses in existing data architecture that affect AI performance or extensibility, working with data, engineering, and product teams to address them.
• Implementation Team Development: Serve as the first engineer on the internal AI implementation team and recommend future roles and hiring priorities to support the transition from outsourced to in-house engineering.
• Participate in recruitment and onboarding, mentor engineers and contractors, and help establish effective delivery practices as the team grows.
• Stakeholder Communication and Innovation: Communicate technical decisions, risks, and engineering tradeoffs clearly to technical, product, engineering, and IT stakeholders.
• Collaborate with architecture, security, data, and product teams to ensure AI initiatives integrate effectively into the broader enterprise environment.
• Evaluate emerging AI tools, platforms, and architectures through practical experimentation and proofs of concept, providing evidence-based recommendations for build-versus-buy and platform decisions.

Requirements

The ideal candidate is an experienced software or data engineer with hands-on expertise delivering AI solutions in production environments. You should combine strong technical execution with sound architectural judgment, effective vendor management, and the ability to establish engineering practices in an evolving AI environment.

• Education: Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or a related field, or equivalent professional experience.
• Professional Experience: At least 8 years of professional software, data, or application development experience, including 2 or more years developing, implementing, or integrating production AI, generative AI, LLM-powered, or agentic AI solutions beyond proof-of-concept work.
• Preferred Experience: 10 or more years of professional software or data engineering experience, including at least 3 years of hands-on AI engineering experience. Experience delivering enterprise-scale AI systems in early-stage or greenfield environments is advantageous.
• Cloud AI Development: Experience delivering production AI solutions in cloud environments, preferably Microsoft Azure, including LLM integration, RAG architecture, and model evaluation.
• Programming and Frameworks: Proficiency in Python and LLM application frameworks and SDKs. Familiarity with multi-agent frameworks such as LangChain, Microsoft AutoGen, or comparable technologies is a plus.
• RAG and Retrieval Systems: Hands-on experience with retrieval-augmented generation, prompt engineering, embedding-based semantic search, vector databases such as Azure AI Search, Pinecone, or Weaviate, and LLM orchestration.
• Knowledge Graphs: Experience designing knowledge graph architectures and ontologies. Direct experience with Graphwise is a strong advantage; familiarity with Neo4j, Amazon Neptune, or Azure Cosmos DB is also valuable.
• AI Evaluation and Observability: Working knowledge of LLM evaluation, RAG performance tracking, prompt versioning, and evaluation frameworks such as Microsoft Foundry, Azure Promptflow, or equivalent tools.
• MLOps and LLMOps: Foundational experience implementing CI/CD for machine learning and LLM pipelines, using Azure DevOps, GitHub Actions, or similar tools. Familiarity with Docker, Kubernetes, model monitoring, token cost tracking, and drift detection is required.
• Experience with data and workflow orchestration tools such as Azure Data Factory or Apache Airflow is a plus.
• Vendor and Delivery Management: Demonstrated ability to assess AI vendor work through architecture reviews, acceptance criteria, code reviews, and delivery validation.
• Data Engineering: Experience developing data ingestion and transformation pipelines, defining data contracts, implementing data quality controls, and collaborating with source-system owners.
• Team Development: Experience establishing engineering practices, contributing to an AI or data function, or helping build and scale an engineering team is beneficial.
• Communication and Collaboration: Strong written and verbal communication skills, including the ability to explain AI architecture decisions, risks, and technical tradeoffs to both technical and non-technical stakeholders.
• Ability to build alignment across product, engineering, data, and security teams without relying on direct authority.
• Strategic Problem-Solving: Strong analytical thinking and the ability to evaluate technical tradeoffs involving innovation, delivery constraints, cost, reliability, and risk.
• Ability to work autonomously in environments with evolving requirements, incomplete information, and competing priorities.
• Responsible AI: Working familiarity with responsible AI principles, relevant regulatory developments, and bias and fairness evaluation for AI-driven recommendation systems is preferred.
• Domain Knowledge: Familiarity with IT governance, cybersecurity risk management, professional certification ecosystems, or mission-driven organizations is advantageous.
• Enterprise Platforms: Experience with Microsoft Azure AI services or equivalent AWS or Google Cloud technologies. Familiarity with Salesforce, Sitecore, and related enterprise platforms is a plus.
• Preferred Certifications: Microsoft Certified: Azure AI App and Agent Developer Associate (AI-103), MLOps Engineer Associate (AI-300), Azure AI Engineer Associate (AI-102), Azure Data Scientist Associate (DP-100), AWS Certified Generative AI Developer – Professional, AWS Machine Learning Engineer – Associate, or Google Cloud Professional Machine Learning Engineer.
• Work Environment: Willingness to attend onsite meetings periodically based on business needs and travel occasionally for company-sponsored events, training, meetings, or other work-related activities.

Benefits

• Annual salary range of $151,047 to $241,653 USD.
• Access to a benefits package; detailed individual benefits and eligibility terms were not specified in the provided job description.
• Opportunity to establish and shape an internal AI engineering capability from its earliest stage.
• Hands-on ownership of production AI systems involving generative AI, knowledge graphs, recommendation engines, and agentic technologies.
• Opportunity to influence AI engineering standards, operational practices, and long-term technology decisions.
• Collaboration with cross-functional teams working on technology, data, product, architecture, and security.
• The opportunity to contribute to a global professional community focused on technology, digital trust, governance, cybersecurity, and emerging technologies.
• Periodic onsite attendance and occasional business travel may be required.