[Hiring] Senior AI Machine Learning Engineer @Techsa

Role Description

• 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.

Qualifications

• 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.

Requirements

• 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).