AI/M Engineer - Generative AI, Agentic AI & MLOps

FULLY REMOTE

ScolerTec is seeking multiple experienced AI/ML Engineers to design, develop, deploy, and operationalize enterprise AI/ML solutions supporting a large-scale cloud data modernization initiative.

The successful candidate will be responsible for delivering advanced analytics, data visualizations, statistical modeling, and AI/ML capabilities, including Generative AI, Agentic AI, predictive models, forecasting, and advanced analytics, to provide data-driven decision-support tools and actionable insights to users and stakeholders.

This role will also provide technical leadership, strategic guidance, and AI/ML tool recommendations to accelerate product development, improve decision automation, and enhance enterprise data and analytics capabilities. The engineer will oversee the design, development, integration, production deployment, and continuous improvement of advanced analytics and AI/ML solutions in alignment with strict federal security, privacy, governance, and compliance requirements.

This is a senior, hands-on engineering role for candidates with experience taking AI/ML solutions from architecture and proof-of-concept through production deployment, monitoring, governance, and continuous improvement.

The ideal candidate brings strong experience across Generative AI, Retrieval-Augmented Generation (RAG), Agentic AI, machine learning, advanced analytics, MLOps/LLMOps, Python, and cloud AI services, with the ability to build secure and scalable solutions in regulated enterprise environments.

Key Responsibilities

• Define and execute AI/ML and advanced analytics strategies aligned with enterprise modernization objectives.
• Design, develop, deploy, and maintain Generative AI, Agentic AI, machine learning, predictive modeling, forecasting, NLP, statistical modeling, visualization, and advanced analytics solutions.
• Build production-grade LLM applications, RAG pipelines, AI agents, multi-step intelligent workflows, and automation capabilities.
• Build and tune machine learning models using appropriate algorithms, architectures, features, and data pipelines.
• Apply statistical and advanced analytical methods to produce actionable insights and decision-support capabilities.
• Evaluate commercial and open-source foundation models, AI frameworks, and emerging technologies.
• Create proofs of concept and transition successful AI/ML solutions into secure, scalable production environments.
• Develop and optimize ML models using Python and frameworks such as PyTorch, TensorFlow, scikit-learn, and XGBoost.
• Design RAG architectures using embeddings, vector databases, semantic/hybrid search, retrieval, reranking, grounding, and metadata filtering.
• Develop Agentic AI solutions using tool calling, workflow orchestration, APIs, enterprise applications, and governed data sources.
• Build MLOps and LLMOps pipelines for automated training, testing, validation, deployment, versioning, monitoring, rollback, and lifecycle management.
• Integrate AI/ML workflows with CI/CD and DevSecOps pipelines.
• Monitor model quality, drift, degradation, latency, hallucination, retrieval performance, and operational health.
• Implement model and LLM evaluation frameworks for accuracy, relevance, groundedness, safety, performance, and cost.
• Implement Responsible AI controls addressing hallucination, prompt injection, data leakage, bias, explainability, privacy, security, and misuse.
• Perform model risk, bias, explainability, security, privacy, and governance assessments.
• Maintain model documentation, architecture, model cards, validation results, lineage, governance artifacts, and lifecycle records.
• Integrate AI solutions with enterprise data platforms, APIs, databases, data lakes, cloud services, and analytics platforms.
• Recommend appropriate models, frameworks, cloud services, vector databases, analytical tools, and AI development technologies.
• Translate business problems into practical AI/ML and advanced analytics use cases.
• Develop reusable AI/ML patterns, standards, and best practices.
• Provide technical leadership and guidance to engineering, analytics, architecture, security, and governance teams.
• Communicate AI/ML architecture, analytical findings, risks, results, and recommendations to both technical and non-technical stakeholders.

Required Qualifications

• Senior-level experience in AI/ML engineering, machine learning, data science, advanced analytics, or related engineering disciplines.
• Strong hands-on programming experience with Python and SQL.
• Strong foundation in machine learning, deep learning, NLP, statistical modeling, predictive analytics, and advanced analytics.
• Experience building and deploying production ML solutions using frameworks such as PyTorch, TensorFlow, scikit-learn, or XGBoost.
• Hands-on experience with Generative AI and LLM applications.
• Production experience implementing RAG pipelines.
• Experience with Agentic AI, AI agents, tool calling, or workflow orchestration.
• Experience with vector databases, embeddings, semantic search, prompt engineering, and LLM evaluation.
• Experience with at least one major cloud AI platform, preferably:
• AWS SageMaker / Amazon Bedrock
• Azure ML / Azure OpenAI
• GCP Vertex AI
• Strong experience with MLOps/LLMOps, including model deployment, CI/CD, monitoring, versioning, evaluation, and lifecycle management.
• Experience moving AI/ML solutions from proof of concept into production.
• Understanding of data governance, model monitoring, security, privacy, Responsible AI, and ethical AI practices.
• Ability to work effectively with data engineers, cloud/platform engineers, cybersecurity teams, architects, analysts, governance teams, and business stakeholders.
• Strong analytical, advisory, presentation, and communication skills.

Experience Requirements

Candidates should meet one of the following experience/education combinations:

• Master's degree with 12+ years overall experience, including 10+ years specialized experience, or
• Bachelor's degree with 14+ years of relevant experience, or
• Associate degree with 16+ years of relevant experience, or
• High school diploma with 18+ years of relevant experience, or
• Equivalent experience consistent with the position requirements.

Recent, demonstrable hands-on AI/ML engineering experience is particularly important.

Federal / Regulated Environment Experience

Experience with one or more of the following is highly desirable:

• NIST AI Risk Management Framework
• FISMA
• FedRAMP
• AI governance
• Security and privacy assessments
• Model risk management
• Authorization / ATO processes
• Data classification and sensitive-data protection
• Federal or other highly regulated enterprise environments

Preferred Technical Experience

• Amazon Bedrock
• AWS SageMaker
• Databricks
• MLflow
• LangChain / LangGraph or comparable LLM frameworks
• Vector databases
• RAG
• Agentic AI
• Predictive modeling and forecasting
• Statistical modeling and advanced analytics
• PyTorch / TensorFlow
• Kubernetes / Docker
• CI/CD and DevSecOps
• Model and LLM observability
• Automated AI evaluation
• Explainable AI
• Responsible AI / AI governance
• Data visualization and analytics platforms

Certification

One of the following is required:

• AWS Certified Machine Learning – Specialty, or
• Google Professional Machine Learning Engineer
• Additional AI governance, AWS, Databricks, data engineering, cloud architecture, or analytics certifications are beneficial.