Senior Lead AIML Engineer

We are seeking AI/ML Engineers who bridge the gap between experimentation and robust production-ready systems. From core predictive modeling to advanced GenAI (RAG agentic workflows fine-tuning) and MLOps we are hiring across three experience tiers:

Open Bands & Profiles

Band 1: Associate AI/ML Engineer (1-3 Years)
Focus: Solid ML foundations strong Python/SQL and growing deployment exposure.
Key Requirements: Supervised/unsupervised learning scikit-learn PyTorch/TensorFlow basic data extraction hands-on exposure to LLM APIs and foundational RAG concepts.

Band 2: Mid-Senior AI/ML Engineer (3-6 Years)
Focus: Independent ownership of end-to-end pipelines from raw data to monitored production endpoints.
Key Requirements: Deep learning fine-tuning production RAG pipelines (LangChain/LlamaIndex vector DBs) MLOps (Docker CI/CD MLflow/W&B) cloud ML platforms (AWS/GCP/Azure) and data drift/monitoring systems.

Band 3: Senior / Lead AI/ML Engineer (6-9 Years)
Focus: Technical leadership high-stakes system design and specialized AI architectures.
Key Requirements: Agentic workflows (multi-step reasoning/tool-use) custom model architectures alignment/fine-tuning (RLHF/DPO LoRA) distributed GPU training scalable ML infrastructure and AI governance/security.
Core Stack

Languages: Python (primary) SQL
Frameworks: PyTorch TensorFlow scikit-learn
GenAI & NLP: LLM APIs RAG LangChain/LlamaIndex Vector DBs Fine-Tuning
Data & MLOps: Spark Docker Kubernetes MLflow AWS/GCP/Azure ML
Specialization Tracks

We also welcome specialists in:
GenAI / LLM & NLP (RAG Agentic Systems Fine-Tuning)
Computer Vision (CNNs Vision Transformers Multi-Modal)
MLOps / ML Infrastructure (Distributed Training Kubernetes Scalable Serving

Required Skills:

PYTHONAZUREMLPYTORCHNLPDOCKERSQLKUBERNETESAIAWSSPARKCI/CDTENSORFLOW