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# Senior AI/ML Data Engineer with AI Platform & RAG Systems - 100% Onsite and In-Person interview in Baltimore,MD

- **Company:** Unisoft Technology Inc
- **Location:** Baltimore, MD, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cloud Engineering, Computer Programming, Continuous Integration, Information Engineering, Data Integration, Extract Transform Load (ETL), Information Retrieval, Python (Programming Language), Machine Learning, OAuth, Octopus Deploy, Performance Tuning, Role-Based Access Control, Azure Machine Learning, Search Technologies, SQL Databases, Management of Software Versions, Enterprise Search, Cloud Platform System, Autoscaling, Retrieval-Augmented Generation, Large Language Models, Snowflake, Multi-Agent Systems, Apache Spark, Generative AI, Agentic-AI, Build Management, Data Lakes, AI Platforms, Pyspark, Kubernetes, Information Technology, Apache Kafka, Data Management, Machine Learning Operations, Video Streaming, Evaluation of Large Language Models, Terraform, Stream Processing, Data Pipelines, Human in the Loop, OpenSearch, Docker
- **Published:** October 18, 2026
- **Apply:** https://www.dice.com/job-detail/5425b480-315e-4ffc-9d4a-6518cc70a614

## About the Role

* 10+ years of overall IT experience with strong focus on AI/ML engineering, data engineering, or platform engineering.
 * Strong hands-on programming experience in Python and SQL.
 * Proven experience building RAG systems, LLM-based applications, and AI orchestration workflows.
 * Strong knowledge of vector databases or vector search technologies such as OpenSearch kNN or similar platforms.
 * Experience building ETL/ELT and ML-ready data pipelines using Spark, PySpark, or similar big data frameworks.
 * Hands-on experience with streaming technologies such as Kafka, Kinesis, or Event Hub.
 * Experience with MLOps tools and deployment frameworks such as MLflow, Docker, Kubernetes, and CI/CD pipelines.
 * Strong experience with AWS and/or Azure cloud ecosystems.
 * Experience implementing observability, monitoring, and evaluation frameworks for AI systems.
 * Knowledge of secure enterprise architecture including RBAC, OAuth2, PII handling, and compliance controls.
 * Bachelor s or Master s degree in Computer Science, Engineering, or a related field.

Preferred Qualifications

 * Experience with enterprise AI platforms such as C3.ai, AWS AI, or Azure AI services.
 * Familiarity with agentic AI, multi-agent systems, and tool-based LLM workflows.
 * Experience with Delta Lake, Snowflake, OpenSearch, and modern cloud data platforms.
 * Exposure to regulated industries such as banking, healthcare, or financial services.
 * Experience with Terraform, ArgoCD, autoscaling frameworks, and cloud-native infrastructure.
 * Ability to translate business requirements into scalable, production-ready AI solutions.

## Description

Seeking a Senior AI/ML Engineer with strong experience in building scalable AI platforms, retrieval-augmented generation (RAG) systems, and production-grade ML/data pipelines for enterprise environments. The ideal candidate will have deep expertise in AI/ML engineering, cloud-native architecture, data engineering, and deploying secure, scalable solutions into production., * Design and build multi-tenant AI platforms, including agentic workflows, RAG services, and LLM orchestration.

 * Develop LLM-powered applications for intelligent automation, enterprise search, and knowledge retrieval.
 * Implement and optimize vector search and retrieval pipelines using OpenSearch kNN, metadata indexing, and hybrid search.
 * Build secure, event-driven ingestion pipelines integrating data lakes, streaming systems, and document processing workflows.
 * Design advanced chunking and document parsing strategies to improve retrieval relevance across multiple file types.
 * Develop LLM evaluation pipelines, golden datasets, custom evaluators, and explainable scoring mechanisms.
 * Implement feedback and human-in-the-loop systems to improve AI performance in production.
 * Establish observability for AI systems, including tracing, latency monitoring, token usage, and model performance insights.
 * Build and optimize batch and real-time data pipelines for ML and analytics workloads.
 * Implement MLOps practices for model training, deployment, versioning, and monitoring.
 * Ensure security, governance, compliance, and responsible AI controls across enterprise deployments.
 * Partner with architecture, product, and security teams to define readiness criteria and production rollout plans.