ML Ops Engineer
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Job Location:
Reading, PA - USA
Monthly Salary:
Not Disclosed
Posted on:
4 hours ago
Vacancies:
1 Vacancy
Job Summary
Location- Reading Pennsylvania Work from Client location 5 days a week
Job Title - ML Ops Engineer
Looking for a pure MLOps Engineer with hands-on experience in Dataiku (Sage Mager is plus).
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.
Qualifications we seek in you!
Minimum Qualifications
Bachelors 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.
Job Title - ML Ops Engineer
Looking for a pure MLOps Engineer with hands-on experience in Dataiku (Sage Mager is plus).
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.
Qualifications we seek in you!
Minimum Qualifications
Bachelors 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.
Key Skills
- ASP.NET
- Health Education
- Fashion Designing
- Fiber
- Investigation
