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AI Summary
This is a remote position.
- Own ML/AI systems end-to-end: data pipelines, model training, serving infrastructure, monitoring, and iteration
- Build LLM-powered applications with custom pipelines, prompt management, evaluation, and optimization
- Implement multi-agent orchestration systems using LangGraph, CrewAI, or AutoGen for autonomous workflows
- Build and optimize RAG pipelines using LlamaIndex with chunking strategies, embedding selection, re-ranking, and evaluation
- Deploy and manage LLM inference infrastructure using vLLM or 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-code canvas (LLM Gateway, RAG Pipeline, Intent Classifier)
- Optimize ML inference for latency and throughput at scale (10K+ QPS)
- Collaborate with Data Engineering and Platform teams to integrate ML systems with data infrastructure
Requirements
- 3+ years of hands-on ML/AI engineering with demonstrated end-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, hybrid search, 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
- Strong Python proficiency: production code structure, async, multiprocessing, profiling, optimization
- Experience with vector databases at scale: OpenSearch k-NN, Qdrant, or Milvus
- Production MLOps experience: MLflow, experiment tracking, model registry, drift monitoring
- Real-time ML inference experience at 1,000+ QPS
Good to Have:
- Experience at AI-first companies or building AI/ML platforms from scratch
- Telco or enterprise data platform background
- Experience with LLM fine-tuning: LoRA, QLoRA, PEFT techniques
- Experience with embedding models: sentence-transformers, fine-tuning for domain
- Kubernetes for ML workload orchestration and GPU scheduling
- Knowledge of PII detection (Presidio) and LLM guardrails (NeMo Guardrails)
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