Senior AI Machine Learning Engineer

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)