- 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)
Skills Required
- 3+ years of hands-on machine learning or AI engineering experience with end-to-end system ownership
- Production experience building LLM-powered applications beyond basic API consumption
- Production experience with agent orchestration using LangGraph, CrewAI, or AutoGen
- Production RAG experience including evaluation metrics, hybrid search, and re-ranking strategies
- Experience building churn, propensity, LTV, segmentation, or recommendation models
- Hands-on experience with Spark batch pipelines and Flink or Kafka Streams real-time pipelines
- Strong Python proficiency, including production code structure, asynchronous programming, multiprocessing, profiling, and optimization
- Experience with vector databases at scale, including OpenSearch k-NN, Qdrant, or Milvus
- Production MLOps experience with MLflow, experiment tracking, model registries, and drift monitoring
- Real-time machine learning inference experience at 1,000 or more queries per second
- Experience at AI-first companies or building AI/ML platforms from scratch
- Telco or enterprise data platform background
- Experience with LLM fine-tuning using LoRA, QLoRA, or PEFT
- Experience with embedding models such as sentence-transformers, including domain fine-tuning
- Kubernetes experience for ML workload orchestration and GPU scheduling
- Knowledge of PII detection using Presidio and LLM guardrails using NeMo Guardrails
What We Do
Techsa is a Cairo-based software services and development company that builds technology solutions tailored to client needs. Its work combines custom and mobile-app development, IT consulting, and software innovation with IoT/data-management platforms and analytics, including AI/ML-driven analysis. It serves sectors such as water and wastewater, logistics, roads, and property maintenance, and also describes itself as a partner for recruiting, onboarding, and managing software teams.
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