Location- Reading, Pennsylvania, Work from Client location, 5 days a week
Max BR - BR/hour
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
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.