MLOps Engineer

Job Title: ML Ops Engineer

Location: Reading, Pennsylvania (Onsite – 5 Days/Week)

Job Description

We are seeking a highly skilled ML Ops Engineer with strong hands-on experience in Dataiku (experience with Amazon SageMaker is a plus). The ideal candidate will have expertise in building and deploying agentic AI systems, RAG pipelines, and scalable MLOps/LLMOps solutions on cloud platforms.

Key Responsibilities

• Design multi-agent architectures by defining agent roles such as planner, researcher, retriever, executor, and reviewer, along with memory strategies and supervisor policies.
• Build high-quality RAG (Retrieval-Augmented Generation) pipelines including ingestion, chunking, embeddings, indexing, and retrieval with proper evaluation and guardrails.
• Productionize AI/ML workloads on AWS using services such as Amazon Bedrock, AWS Lambda, Amazon API Gateway, Amazon S3, Amazon DynamoDB, Amazon OpenSearch Service, and AWS Step Functions.
• Implement MLOps/LLMOps best practices including CI/CD automation, GitOps workflows, containerization using Docker and Kubernetes, infrastructure-as-code, secrets management, and deployment strategies.
• Develop observability and evaluation frameworks by instrumenting telemetry (traces, token usage, latency) and building dashboards using Grafana or Amazon CloudWatch.
• Ensure reliability and scalability by implementing caching, rate limiting, queue management, and proactive detection of drift or performance degradation.
• Collaborate with DevOps, infrastructure, and data engineering teams to deliver robust AI/ML solutions and maintain clear documentation, SLIs/SLOs, and operational runbooks.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (or equivalent experience).
• Proven experience building agentic systems and RAG pipelines in production environments.
• Strong cloud experience for AI/ML workloads with familiarity in Amazon Bedrock or similar LLM platforms.
• Hands-on experience with CI/CD pipelines, Git, Docker, and Kubernetes.
• Knowledge of data governance and model lifecycle management within the MLOps/LLMOps ecosystem.
• Strong communication and collaboration skills with the ability to work in cross-functional teams.
• Passion for Generative AI and agent-based AI solutions.

Preferred / Nice to Have

• Experience with AWS Bedrock Agents, Knowledge Bases, and Flows.
• Hands-on experience with Dataiku platform governance, approvals, artifacts, and MLOps deployment workflows.
• Experience with Amazon SageMaker for custom model hosting.
• Familiarity with agent frameworks such as LangGraph, crewAI, Semantic Kernel, or AutoGen.
• Knowledge of evaluation frameworks including guardrails, groundedness checks, and hallucination detection.
• Relevant Dataiku certifications such as ML Practitioner, Advanced Designer, or MLOps Practitioner.