AI/ML Engineer @ Malvern, PA (3 days’ on-site required)--Must be local

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Saxon Global Inc., is seeking the following. Apply via Dice today!

Hello,

I am Mohammed Dastagir with Saxon Global Inc wanted to let you know about the job opportunity for AI/ML Engineer position if interested please share your updated resume along with expecting rate.

Number of Openings: 2

Title: AI/ML Engineer

Location: Malvern, PA (3 days’ on-site required) Must live within 40 minutes of Malvern, PA

Duration: 12 months

Open for C2C/W2/ H1 transfers

Core Responsibilities

• Agentic AI & MCP Integration: Implement agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for secure tool orchestration.
• Generative AI Development: Build LLM-based applications with RAG, structured output, and evaluation frameworks.
• Agentic Cloud Deployment & Integration: Design and deploy agentic AI services in cloud environments, integrating models, tools, APIs, and data sources to deliver scalable, autonomous workflows.
• Databricks & Lakehouse Engineering: Develop and optimize ML and GenAI workloads using Databricks, including Spark‑based data pipelines, feature engineering, and model training/inference on the Lakehouse platform.
• Unity Catalog & Governance: Implement Unity Catalog for centralized data, model, and feature governance, ensuring secure access control, lineage tracking, and compliance across ML and GenAI assets.
• AWS ML Engineering: Deploy models using SageMaker pipelines, ECS/ECR, Lambda; manage CI/CD and monitoring.
• Security & Identity: Integrate Okta/JWT token for API and service authentication; enforce token validation and claims.
• Governance : Deliver artifacts required by MDLC/MPLC (Model Documents, Data Dictionary, Monitoring Plan).
• Collaboration: Partner with PO, and business stakeholders to align solutions with objectives.
  
  

Responsibilities

• Design, develop, and optimize complex data pipelines using machine learning engineering best practices to ensure scalability, efficiency, and reliability.
• Develop and implement robust MLOps pipeline to support the deployment, monitoring, and lifecycle management of AI/ML models in production environments.
• Integrate and maintain data and model pipelines, proactively diagnosing data quality issues and documenting assumptions.
• Collaborate closely with data scientists to validate model-ready datasets and ensure thorough, accurate feature documentation.
• Conduct exploratory data analysis and discovery on raw data sources, incorporating business context to support model development.
• Track data lineage and perform root cause analysis during early-stage exploration or issue resolution.
• Partner with internal stakeholders to understand business processes and translate them into scalable analytical solutions.
• Develop and maintain model monitoring scripts, investigate alerts, and coordinate timely resolutions.
• Act as a subject matter expert in machine learning engineering on cross-functional teams, contributing to high-impact initiatives.
• Stay current with advancements in AI/ML and evaluate their applicability to business challenges.
  
  

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
• 6 years of experience across Artificial Intelligence (AI) / Machine Learning (ML) engineering, data engineering, and MLOps implementation, including:
• Designing and deploying production-grade ML systems.
• Building scalable data pipelines and ML workflows.
• Managing model lifecycle in cloud environments.
• Proficient in Python and familiar with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Hands‑on experience with Databricks, including:
• Spark‑based data processing and feature engineering
• Databricks ML/MLflow for experiment tracking and model management
• Integrating Databricks with cloud‑native ML services
• Experience implementing Unity Catalog for centralized governance of data, features, and models, including access controls, lineage, and auditability.
• Strong understanding and experience in AWS Machine Learning Stack including:
• AWS SageMaker
• AWS Glue
• AWS Bedrock
• AWS Data Pipelines
• AWS Lambda Functions
• Experience with Generative AI model development builing LLM based applications with RAG.
• Experience implementing agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for orchestration.
• Knowledge of React UI, GraphDB, and GenAI model performance evaluation
• Experience with CI/CD, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes).
• Solid grasp of software engineering principles including testing, version control (e.g., Git), and security.
• Familiarity with the Machine Learning Development Lifecycle (MDLC) and best practices for reproducibility and scalability.
• Strong communication and collaboration skills, with experience working across technical and business teams.
• Ability to anticipate ambiguity and devise scalable solutions to address it.
  
  

Nice to Have

• Knowledge of data governance, model explainability, and responsible AI practices.
  
  

Mohammed Dastagir

Sr Resource Manager

Saxon Global Inc.

a:

Linked in:

w: e: dastagir.m