ML Ops Engineer

Reading, PA, US • Posted 20 hours ago • Updated 31 minutes ago

Contract Corp To Corp

Contract W2

Contract Independent

On-site

Fitment

Dice Job Match Score™

✨ Finding the perfect fit...

Job Details

Skills

Summary

BuzzClan is an elite business consulting firm collaborating to provide software, advisory & implementation services. BuzzClan is a certified partner for most of the tier 1 cloud, hardware & software providers. Being a vertically integrated solutions company, BuzzClan is known for their capability in the IT Services space.

Job Title: ML Ops Engineer

Job Location : Reading, Pennsylvania, - Onsite

Responsibilities
Design multi-agent architectures: define agent roles (planner, researcher, retriever, executor, reviewer), toolboxes, handoffs, memory strategy (short/long-term), and supervisor policies for safe collaboration.
Build high-quality RAG: implement ingestion, chunking, embeddings, indexing, and retrieval with evaluation (precision/recall, groundedness, hallucination checks), guardrails, and citations.
Productionize on AWS: leverage services like Bedrock (Agents/Knowledge Bases/Flows), Lambda, API Gateway, S3, DynamoDB, OpenSearch/Vector DB, Step Functions, and CloudWatch for tracing and alerts.
MLOps/LLMOps: automate CI/CD (GitOps), containerization (Docker/Kubernetes), infra-as-code, secrets/IAM, blue green/rollbacks, and data/feature pipelines.
Observability & evaluation: instrument telemetry (traces, token/cost, latency), build dashboards (Grafana/CloudWatch), add human-in-the-loop review, A/B testing, and continuous offline/online evals.
Operate reliably at scale: implement caching, rate-limit management, queueing, idempotency, and backoff; proactively detect drift and degradation.
Collaborate & communicate partner with infra/DevOps/data/architecture teams; document designs, SLIs/SLOs, runbooks; present status and insights to technical and non-technical stakeholders.
Minimum Qualifications
Bachelor's degree in computer science, Data Science, Engineering, or related field-or equivalent experience.
Proven experience building agentic systems (single or multi-agent) and RAG pipelines in production.
Strong cloud background for AI/ML workloads; familiarity with Bedrock or equivalent LLM platforms.
Solid CI/CD and containerization skills (Git, Docker, Kubernetes) and infra-as-code fundamentals.
Knowledge of data governance and model accountability throughout the MLOps/LLMOps lifecycle.
Excellent communication, collaboration, and problem-solving skills; ability to work independently and within cross-functional teams.
Passion for Generative AI and the impact of agent-based solutions across industries.
Preferred / Good to Have
Experience with AWS Bedrock Agents/Knowledge Bases/Flows, OpenSearch (or other vector databases), Step Functions, Lambda, API Gateway, DynamoDB, S3.
Dataiku platform exposure-govern, approvals, artifacts, MLOps deployment flows; SageMaker for custom model hosting.
Familiarity with agent frameworks (e.g., LangGraph, crewAI, Semantic Kernel, AutoGen) and evaluation frameworks (guardrails, groundedness, hallucination checks).
Covered these Dataiku Certifications (nice to have): ML Practitioner, Advanced Designer, MLOps Practitioner.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 90709156
Position Id: 2026-17150
Posted 20 hours ago

Create job alert

Never miss an opportunity! Create an alert based on the job you applied for.

ML Ops Engineer

Dice Job Match Score™

Job Details

Skills

Summary

Similar Jobs