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Techsa

2 jobs near Columbus, OH

Techsa

Senior AI Machine Learning Engineer

Whitehall, PA · On-site

$120 - $190/hr

* - Own ML/AI systems end-to-end: data pipelines, modeltraining, serving infrastructure, monitoring, and iteration * - Build LLM-powered applications with custom pipelines,prompt management, evaluation ...

New

Techsa

Senior Backend Engineer

Whitehall, PA · On-site

$110 - $160/hr

- Design and build scalable backend services and APIs using Javaand Spring Boot - Develop microservices architecture for high-throughput,low-latency data processing - Build event-driven systems using ...

New

Senior AI Machine Learning Engineer

Techsa

Whitehall, PA • On-site

$120 - $190/hr

Other

Posted yesterday

New

Job description

  • - Own ML/AI systems end-to-end: data pipelines, modeltraining, serving infrastructure, monitoring, and iteration
  • - Build LLM-powered applications with custom pipelines,prompt management, evaluation, and optimization
  • - Implement multi-agent orchestration systems usingLangGraph, CrewAI, or AutoGen for autonomous workflows
  • - Build and optimize RAG pipelines using LlamaIndex withchunking strategies, embedding selection, re-ranking, and evaluation
  • - Deploy and manage LLM inference infrastructure using vLLMor Ollama for on-premise sovereign deployments
  • - Build traditional ML scoring models: churn prediction,propensity scoring, LTV estimation, next-best-action
  • - Design and build feature pipelines using Apache Flink(streaming) and Spark (batch) for real-time and batch ML
  • - Implement MLOps practices: model versioning, registry,drift monitoring, A/B testing, and staged rollouts
  • - Design and implement AI operators for visual low-codecanvas (LLM Gateway, RAG Pipeline, Intent Classifier)
  • - Optimize ML inference for latency and throughput at scale(10K+ QPS)
  • - Collaborate with Data Engineering and Platform teams tointegrate ML systems with data infrastructure
Requirements
  • - 3+ years of hands-on ML/AI engineering with demonstratedend-to-end system ownership
  • - Production experience building LLM-powered applications(not just API consumption)
  • - Hands-on experience with agent orchestration: LangGraph,CrewAI, or AutoGen in production
  • - Production RAG experience with evaluation metrics, hybridsearch, and re-ranking strategies
  • - Experience building ML models: churn, propensity, LTV,segmentation, recommendation systems
  • - Hands-on experience with data pipelines: Spark for batch,Flink or Kafka Streams for real-time
  • - Experience with vector databases at scale: OpenSearchk-NN, Qdrant, or Milvus
  • - Real-time ML inference experience at 1,000+ QPS
Good to Have
  • - Experience at AI-first companies or building AI/MLplatforms from scratch
  • - Telco or enterprise data platform background
  • - Experience with LLM fine-tuning: LoRA, QLoRA, PEFTtechniques
  • - Experience with embedding models: sentence-transformers,fine-tuning for domain
  • - Kubernetes for ML workload orchestration and GPUscheduling
  • - Knowledge of PII detection (Presidio) and LLM guardrails(NeMo Guardrails)
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