- 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

- 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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